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Abstract Ischemia-induced retinopathy is a hallmark finding of common visual disorders including diabetic retinopathy (DR) and central retinal artery and vein occlusions. Treatments for ischemic retinopathies fail to improve clinical outcomes and the design of new therapies will depend on understanding the underlying disease mechanisms. Histone deacetylases (HDACs) are an enzyme class that removes acetyl groups from histone and non-histone proteins, thereby regulating gene expression and protein function. HDACs have been implicated in retinal neurovascular injury in preclinical studies in which nonspecific HDAC inhibitors mitigated retinal injury. Histone deacetylase 3 (HDAC3) is a class I histone deacetylase isoform that plays a central role in the macrophage inflammatory response. We recently reported that myeloid cells upregulate HDAC3 in a mouse model of retinal ischemia-reperfusion (IR) injury. However, whether this cellular event is an essential contributor to retinal IR injury is unknown. In this study, we explored the role of myeloid HDAC3 in ischemia-induced retinal neurovascular injury by subjecting myeloid-specific HDAC3 knockout (M-HDAC3 KO) and floxed control mice to retinal IR. The M-HDAC3 KO mice were protected from retinal IR injury as shown by the preservation of inner retinal neurons, vascular integrity, and retinal thickness. Electroretinography confirmed that this neurovascular protection translated to improved retinal function. The retinas of M-HDAC3 KO mice also showed less proliferation and infiltration of myeloid cells after injury. Interestingly, myeloid cells lacking HDAC3 more avidly engulfed apoptotic cells in vitro and after retinal IR injury in vivo compared to wild-type myeloid cells, suggesting that HDAC3 hinders the reparative phagocytosis of dead cells, a process known as efferocytosis. Further mechanistic studies indicated that although HDAC3 KO macrophages upregulate the reparative enzyme arginase 1 (A1) that enhances efferocytosis, the inhibitory effect of HDAC3 on efferocytosis is not solely dependent on A1. Finally, treatment of wild-type mice with the HDAC3 inhibitor RGFP966 ameliorated the retinal neurodegeneration and thinning caused by IR injury. Collectively, our data show that HDAC3 deletion enhances macrophage-mediated efferocytosis and protects against retinal IR injury, suggesting that inhibiting myeloid HDAC3 holds promise as a novel therapeutic strategy for preserving retinal integrity after ischemic insult.

Abstract The prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its semi-brittle nature. Existing atomistic simulations of the crack-tip under mode-I loading based on empirical interatomic potentials yield contradicting predictions and artificial mechanisms. To enable fracture prediction with quantum accuracy, we develop a Gaussian approximation potential (GAP) using an active learning strategy by extending a density functional theory (DFT) database of ferromagnetic bcc iron. We apply the active learning algorithm and obtain a Fe GAP model with a converged model uncertainty over a broad range of stress intensity factors (SIFs) and for four crack systems. The learning efficiency of the approach is analysed, and the predicted critical SIFs are compared with Griffith and Rice theories. The simulations reveal that cleavage along the original crack plane is the atomistic fracture mechanism for {100} and {110} crack planes at T = 0 K, thus settling a long-standing issue. Our work also highlights the need for a multiscale approach to predicting fracture and intrinsic ductility, whereby finite temperature, finite loading rate effects and pre-existing defects (e.g., nanovoids, dislocations) should be taken explicitly into account.

Abstract We introduce a training protocol for developing machine learning force fields (MLFFs), capable of accurately determining energy barriers in catalytic reaction pathways. The protocol is validated on the extensively explored hydrogenation of carbon dioxide to methanol over indium oxide. With the help of active learning, the final force field obtains energy barriers within 0.05 eV of Density Functional Theory. Thanks to the computational speedup, not only do we reduce the cost of routine in-silico catalytic tasks, but also find an alternative path for the previously established rate-limiting step, with a 40% reduction in activation energy. Furthermore, we illustrate the importance of finite temperature effects and compute free energy barriers. The transferability of the protocol is demonstrated on the experimentally relevant, yet unexplored, top-layer reduced indium oxide surface. The ability of MLFFs to enhance our understanding of extensively studied catalysts underscores the need for fast and accurate alternatives to direct ab-initio simulations.

Abstract Data-driven interatomic potentials have emerged as a powerful tool for approximating ab initio potential energy surfaces. The most time-consuming step in creating these interatomic potentials is typically the generation of a suitable training database. To aid this process hyperactive learning (HAL), an accelerated active learning scheme, is presented as a method for rapid automated training database assembly. HAL adds a biasing term to a physically motivated sampler (e.g. molecular dynamics) driving atomic structures towards uncertainty in turn generating unseen or valuable training configurations. The proposed HAL framework is used to develop atomic cluster expansion (ACE) interatomic potentials for the AlSi10 alloy and polyethylene glycol (PEG) polymer starting from roughly a dozen initial configurations. The HAL generated ACE potentials are shown to be able to determine macroscopic properties, such as melting temperature and density, with close to experimental accuracy.

Abstract Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for studying molecular mechanisms in the condensed phase, however, they are too expensive to capture many key properties that converge slowly with respect to simulation length and time scales. Machine learning (ML) approaches which reach the accuracy of ab initio simulation, and which are, at the same time, sufficiently affordable hold the key to bridging this gap. In this work we present a robust ML potential for the EC:EMC binary solvent, a key component of liquid electrolytes in rechargeable Li-ion batteries. We identify the necessary ingredients needed to successfully model this liquid mixture of organic molecules. In particular, we address the challenge posed by the separation of scale between intra- and inter-molecular interactions, which is a general issue in all condensed phase molecular systems.

IntroductionAlthough both COVID-19 and non-COVID-19 ARDS can be accompanied by significantly increased levels of circulating cytokines, the former significantly differs from the latter by its higher vasculopathy, characterized by increased oxidative stress and coagulopathy in lung capillaries. This points towards the existence of SARS-CoV2-specific factors and mechanisms that can sensitize the endothelium towards becoming dysfunctional. Although the virus is rarely detected within endothelial cells or in the circulation, the S1 subunit of its spike protein, which contains the receptor binding domain (RBD) for human ACE2 (hACE2), can be detected in plasma from COVID-19 patients and its levels correlate with disease severity. It remains obscure how the SARS-CoV2 RBD exerts its deleterious actions in lung endothelium and whether there are mechanisms to mitigate this.MethodsIn this study, we use a combination of in vitro studies in RBD-treated human lung microvascular endothelial cells (HL-MVEC), including electrophysiology, barrier function, oxidative stress and human ACE2 (hACE2) surface protein expression measurements with in vivo studies in transgenic mice globally expressing human ACE2 and injected with RBD.ResultsWe show that SARS-CoV2 RBD impairs endothelial ENaC activity, reduces surface hACE2 expression and increases reactive oxygen species (ROS) and tissue factor (TF) generation in monolayers of HL-MVEC, as such promoting barrier dysfunction and coagulopathy. The TNF-derived TIP peptide (a.k.a. solnatide, AP301) -which directly activates ENaC upon binding to its a subunit- can override RBD-induced impairment of ENaC function and hACE2 expression, mitigates ROS and TF generation and restores barrier function in HL-MVEC monolayers. In correlation with the increased mortality observed in COVID-19 patients co-infected with S. pneumoniae, compared to subjects solely infected with SARS-CoV2, we observe that prior intraperitoneal RBD treatment in transgenic mice globally expressing hACE2 significantly increases fibrin deposition and capillary leak upon intratracheal instillation of S. pneumoniae and that this is mitigated by TIP peptide treatment.

Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the same time, the energy landscapes need to be accurately mapped, as small errors in barriers can lead to large deviations in reaction probabilities. This brings a particularly interesting challenge for machine learning interatomic potentials, which are becoming well-established tools to accelerate molecular dynamics simulations. We compare state-of-the-art machine learning interatomic potentials with a particular focus on their inference performance on CPUs and suitability for high throughput simulation of reactive chemistry at surfaces. The considered models include polarizable atom interaction neural networks (PaiNN), recursively embedded atom neural networks (REANN), the MACE equivariant graph neural network, and atomic cluster expansion potentials (ACE). The models are applied to a dataset on reactive molecular hydrogen scattering on low-index surface facets of copper. All models are assessed for their accuracy, time-to-solution, and ability to simulate reactive sticking probabilities as a function of the rovibrational initial state and kinetic incidence energy of the molecule. REANN and MACE models provide the best balance between accuracy and time-to-solution and can be considered the current state-of-the-art in gas-surface dynamics. PaiNN models require many features for the best accuracy, which causes significant losses in computational efficiency. ACE models provide the fastest time-to-solution, however, models trained on the existing dataset were not able to achieve sufficiently accurate predictions in all cases.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While recent studies have demonstrated that SARS-CoV-2 may enter kidney and colon epithelial cells by inducing receptor-independent macropinocytosis, it remains unknown whether this process also occurs in cell types directly relevant to SARS-CoV-2-associated lung pneumonia, such as alveolar epithelial cells and macrophages. The goal of our study was to investigate the ability of SARS-CoV-2 spike protein subunits to stimulate macropinocytosis in human alveolar epithelial cells and primary human and murine macrophages. Flow cytometry analysis of fluid-phase marker internalization demonstrated that SARS-CoV-2 spike protein subunits S1, the receptor-binding domain (RBD) of S1, and S2 stimulate macropinocytosis in both human and murine macrophages in an angiotensin-converting enzyme 2 (ACE2)-independent manner. Pharmacological and genetic inhibition of macropinocytosis substantially decreased spike-protein-induced fluid-phase marker internalization in macrophages both in vitro and in vivo. High-resolution scanning electron microscopy (SEM) imaging confirmed that spike protein subunits promote the formation of membrane ruffles on the dorsal surface of macrophages. Mechanistic studies demonstrated that SARS-CoV-2 spike protein stimulated macropinocytosis via NADPH oxidase 2 (Nox2)-derived reactive oxygen species (ROS) generation. In addition, inhibition of protein kinase C (PKC) and phosphoinositide 3-kinase (PI3K) in macrophages blocked SARS-CoV-2 spike-protein-induced macropinocytosis. To our knowledge, these results demonstrate for the first time that SARS-CoV-2 spike protein subunits stimulate macropinocytosis in macrophages. These results may contribute to a better understanding of SARS-CoV-2 infection and COVID-19 pathogenesis.

Abstract Many atomic descriptors are currently limited by their unfavourable scaling with the number of chemical elements S e.g. the length of body-ordered descriptors, such as the SOAP power spectrum (3-body) and the (ACE) (multiple body-orders), scales as (N S) ν where ν + 1 is the body-order and N is the number of radial basis functions used in the density expansion. We introduce two distinct approaches which can be used to overcome this scaling for the SOAP power spectrum. Firstly, we show that the power spectrum is amenable to lossless compression with respect to both S and N, so that the descriptor length can be reduced from $${{{\mathcal{O}}}}({N}^{2}{S}^{2})$$ O ( N 2 S 2 ) to $${{{\mathcal{O}}}}\left(NS\right)$$ O N S . Secondly, we introduce a generalised SOAP kernel, where compression is achieved through the use of the total, element agnostic density, in combination with radial projection. The ideas used in the generalised kernel are equally applicably to any other body-ordered descriptors and we demonstrate this for the (ACSF).

Abstract Mutations in the NF1 tumor suppressor gene are linked to arteriopathy. Nf1 heterozygosity (Nf1+/–) results in robust neointima formation, similar to humans, and myeloid-restricted Nf1+/– recapitulates this phenotype via MEK-ERK activation. Here we define the contribution of myeloid subpopulations to NF1 arteriopathy. Neutrophils from WT and Nf1+/– mice were functionally assessed in the presence of MEK and farnesylation inhibitors in vitro and neutrophil recruitment to lipopolysaccharide was assessed in WT and Nf1+/– mice. Littermate 12–15 week-old male wildtype and Nf1+/– mice were subjected to carotid artery ligation and provided either a neutrophil depleting antibody (1A8), liposomal clodronate to deplete monocytes/macrophages, or PD0325901 and neointima size was assessed 28 days after injury. Bone marrow transplant experiments assessed monocyte/macrophage mobilization during neointima formation. Nf1+/– neutrophils exhibit enhanced proliferation, migration, and adhesion via p21Ras activation of MEK in vitro and in vivo. Neutrophil depletion suppresses circulating Ly6Clow monocytes and enhances neointima size, while monocyte/macrophage depletion and deletion of CCR2 in bone marrow cells abolish neointima formation in Nf1+/– mice. Taken together, these findings suggest that neurofibromin-MEK-ERK activation in circulating neutrophils and monocytes during arterial remodeling is nuanced and points to important cross-talk between these populations in the pathogenesis of NF1 arteriopathy.

Traditionally, interatomic potentials assume local bond formation supplemented by long-range electrostatic interactions when necessary. This ignores intermediate-range multiatom interactions that arise from the relaxation of the electronic structure. Here, we present the multilayer atomic cluster expansion (ml-ACE) that includes collective, semi-local multiatom interactions naturally within its remit. We demonstrate that ml-ACE significantly improves fit accuracy and efficiency compared to a local expansion on selected examples and provide physical intuition to understand this improvement.

The detection of superoxide anion (O2●−) in biological tissues remains challenging. Barriers to convenient and reproducible measurements include expensive equipment, custom probes, and the need for high sensitivity and specificity. The luminol derivative, L-012, has been used to measure O2●− since 1993 with mixed results and concerns over specificity. The goal of this study was to better define the conditions for use and their specificity. We found that L-012 coupled with depolymerized orthovanadate, a relatively impermeable tyrosine phosphatase inhibitor, yielded a highly sensitive approach to detect extracellular O2●−. In O2●− producing HEK-NOX5 cells, orthovanadate increased L-012 luminescence 100-fold. The combination of L-012 and orthovanadate was highly sensitive, stable, scalable, completely reversed by superoxide dismutase, and selective for O2●− generating NOXes versus NOX4, which produces H2O2. Moreover, there was no signal from cells transfected with NOS3 (NO●) and NOS2(ONOO−). To exclude the effects of altered tyrosine phosphorylation, O2●− was detected using non-enzymatic synthesis with phenazine methosulfate and via novel coupling of L-012 with niobium oxalate, which was less active in inducing tyrosine phosphorylation. Overall, our data shows that L-012 coupled with orthovanadate or other periodic group 5 salts yields a reliable, sensitive, and specific approach to measuring extracellular O2●− in biological systems.

Abstract The atomic cluster expansion is a general polynomial expansion of the atomic energy in multi-atom basis functions. Here we implement the atomic cluster expansion in the performant C++ code PACE that is suitable for use in large-scale atomistic simulations. We briefly review the atomic cluster expansion and give detailed expressions for energies and forces as well as efficient algorithms for their evaluation. We demonstrate that the atomic cluster expansion as implemented in PACE shifts a previously established Pareto front for machine learning interatomic potentials toward faster and more accurate calculations. Moreover, general purpose parameterizations are presented for copper and silicon and evaluated in detail. We show that the Cu and Si potentials significantly improve on the best available potentials for highly accurate large-scale atomistic simulations.

Abstract We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTPs) are polynomial-like functions of interatomic distances and angles. The Gaussian approximation potential (GAP) framework uses kernel regression, and we use the smooth overlap of atomic position (SOAP) representation of atomic neighborhoods that consist of a complete set of rotational and permutational invariants provided by the power spectrum of the spherical Fourier transform of the neighbor density. Both types of potentials give excellent accuracy for a wide range of compositions, competitive with the accuracy of cluster expansion, a benchmark for this system. While both models are able to describe small deformations away from the lattice positions, SOAP-GAP excels at transferability as shown by sensible transformation paths between configurations, and MTP allows, due to its lower computational cost, the calculation of compositional phase diagrams. Given the fact that both methods perform nearly as well as cluster expansion but yield off-lattice models, we expect them to open new avenues in computational materials modeling for alloys.

Atomistic simulations of phosphorus represent a challenge due to the element’s highly diverse allotropic structures. Here the authors propose a general-purpose machine-learning force field for elemental phosphorus, which can describe a broad range of relevant bulk and nanostructured allotropes.

Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell molecules for machine-learning predictions of reaction energies.

Alveolar-capillary leak is a hallmark of the acute respiratory distress syndrome (ARDS), a potentially lethal complication of severe sepsis, trauma and pneumonia, including COVID-19. Apart from barrier dysfunction, ARDS is characterized by hyper-inflammation and impaired alveolar fluid clearance (AFC), which foster the development of pulmonary permeability edema and hamper gas exchange. Tumor Necrosis Factor (TNF) is an evolutionarily conserved pleiotropic cytokine, involved in host immune defense against pathogens and cancer. TNF exists in both membrane-bound and soluble form and its mainly -but not exclusively- pro-inflammatory and cytolytic actions are mediated by partially overlapping TNFR1 and TNFR2 binding sites situated at the interface between neighboring subunits in the homo-trimer. Whereas TNFR1 signaling can mediate hyper-inflammation and impaired barrier function and AFC in the lungs, ligand stimulation of TNFR2 can protect from ventilation-induced lung injury. Spatially distinct from the TNFR binding sites, TNF harbors within its structure a lectin-like domain that rather protects lung function in ARDS. The lectin-like domain of TNF -mimicked by the 17 residue TIP peptide- represents a physiological mediator of alveolar-capillary barrier protection. and increases AFC in both hydrostatic and permeability pulmonary edema animal models. The TIP peptide directly activates the epithelial sodium channel (ENaC) -a key mediator of fluid and blood pressure control- upon binding to its α subunit, which is also a part of the non-selective cation channel (NSC). Activity of the lectin-like domain of TNF is preserved in complexes between TNF and its soluble TNFRs and can be physiologically relevant in pneumonia. Antibody- and soluble TNFR-based therapeutic strategies show considerable success in diseases such as rheumatoid arthritis, psoriasis and inflammatory bowel disease, but their chronic use can increase susceptibility to infection. Since the lectin-like domain of TNF does not interfere with TNF’s anti-bacterial actions, while exerting protective actions in the alveolar-capillary compartments, it is currently evaluated in clinical trials in ARDS and COVID-19. A more comprehensive knowledge of the precise role of the TNFR binding sites versus the lectin-like domain of TNF in lung injury, tissue hypoxia, repair and remodeling may foster the development of novel therapeutics for ARDS.

In many complex molecular systems, the macroscopic ensemble’s properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships between local structural environments and the dynamical events originating from them would allow unveiling microscopic-level structure-dynamics relationships fundamental to understand the macroscopic behavior of complex systems. Here we show that, by coupling advanced structural (e.g. Smooth Overlap of Atomic Positions, SOAP) with local dynamical descriptors (e.g. Local Environment and Neighbor Shuffling, LENS) in a unique dataset, it is possible to improve both individual SOAP- and LENS-based analyses, obtaining a more complete characterization of the system under study. As representative examples, we use various molecular systems with diverse internal structural dynamics. On the one hand, we demonstrate how the combination of structural and dynamical descriptors facilitates decoupling relevant dynamical fluctuations from noise, overcoming the intrinsic limits of the individual analyses. Furthermore, machine learning approaches also allow extracting from such combined structural/dynamical dataset useful microscopic-level relationships, relating key local dynamical events (e.g. LENS fluctuations) occurring in the systems to the local structural (SOAP) environments they originate from. Given its abstract nature, we believe that such an approach will be useful in revealing hidden microscopic structure-dynamics relationships fundamental to rationalize the behavior of a variety of complex systems, not necessarily limited to the atomistic and molecular scales.

Background: The increasingly common applications of machine-learning schemes to atomic-scale simulations have triggered efforts to better understand the mathematical properties of the mapping between the Cartesian coordinates of the atoms and the variety of representations that can be used to convert them into a finite set of symmetric descriptors or features. Methods: Here, we analyze the sensitivity of the mapping to atomic displacements, using a singular value decomposition of the Jacobian of the transformation to quantify the sensitivity for different configurations, choice of representations and implementation details. Results: We show that the combination of symmetry and smoothness leads to mappings that have singular points at which the Jacobian has one or more null singular values (besides those corresponding to infinitesimal translations and rotations). This is in fact desirable, because it enforces physical symmetry constraints on the values predicted by regression models constructed using such representations. However, besides these symmetry-induced singularities, there are also spurious singular points, that we find to be linked to the incompleteness of the mapping, i.e. the fact that, for certain classes of representations, structurally distinct configurations are not guaranteed to be mapped onto different feature vectors. Additional singularities can be introduced by a too aggressive truncation of the infinite basis set that is used to discretize the representations. Conclusions: These results exemplify the subtle issues that arise when constructing symmetric representations of atomic structures, and provide conceptual and numerical tools to identify and investigate them in both benchmark and realistic applications.

Machine learning: Modelling atomic systems to make property predictions A method for representing atomic systems for machine learning is shown that can provide access to the physical properties of these systems. Machine learning is a powerful tool for finding correlations but when used to look at real-word systems, the complexity of the models often limits the amount of information that can be extracted about the underlying physics. An international team of researchers led by Conrad Rosenbrock from Brigham Young University now present a machine learning-based approach for modelling atomic systems that can provide insight into the physical building blocks that influence them. They demonstrate the power of their approach by examining the predictive performance of several machine learning models, providing connections between the structure and behaviour of grain boundaries in crystalline materials, which could be extended to other systems that involve local structural changes.

Neurofibromin, the protein product of the neurofibromatosis type 1 (NF1) tumor suppressor gene, is a negative regulator of Ras signaling. Patients with mutations in NF1 have a strong predisposition for cardiovascular disease, which contributes to their early mortality. Nf1 heterozygous (Nf1+/−) bone marrow to wild type chimeras and mice with heterozygous recombination of Nf1 in myeloid cells recapitulate many of the vascular phenotypes observed in Nf1+/− mutants. Although these results suggest that macrophages play a central role in NF1 vasculopathy, the underlying mechanisms are currently unknown. In the present study, we employed macrophages isolated from either Nf1+/− or Lysm Cre+/Nf1f/f mice to test the hypothesis that loss of Nf1 stimulates macropinocytosis in macrophages. Scanning electron microscopy and flow cytometry analysis of FITC-dextran internalization demonstrated that loss of Nf1 in macrophages stimulates macropinocytosis. We next utilized various cellular and molecular approaches, pharmacological inhibitors and genetically modified mice to identify the signaling mechanisms mediating macropinocytosis in Nf1-deficient macrophages. Our results indicate that loss of Nf1 stimulates PKCδ-mediated p47phox phosphorylation via RAS activation, leading to increased NADPH oxidase 2 activity, reactive oxygen species generation, membrane ruffling and macropinocytosis. Interestingly, we also found that Nf1-deficient macrophages internalize exosomes derived from angiotensin II-treated endothelial cells via macropinocytosis in vitro and in the peritoneal cavity in vivo. As a result of exosome internalization, Nf1-deficient macrophages polarized toward an inflammatory M1 phenotype and secreted increased levels of proinflammatory cytokines compared to controls. In conclusion, the findings of the present study demonstrate that loss of Nf1 stimulates paracrine endothelial to myeloid cell communication via macropinocytosis, leading to proinflammatory changes in recipient macrophages. Keywords: Neurofibromin, Macropinocytosis, NADPH oxidase, Macrophages and exosomes

Arterial accumulation of plasma-derived LDL and its subsequent oxidation contributes to atherosclerosis. Lymphatic vessel (LV)-mediated removal of arterial cholesterol has been shown to reduce atherosclerotic lesion formation. However, the precise mechanisms that regulate LV density and function in atherosclerotic vessels remain to be identified. The aim of this study was to investigate the role of native LDL (nLDL) and oxidized LDL (oxLDL) in modulating lymphangiogenesis and underlying molecular mechanisms. Western blotting and immunostaining experiments demonstrated increased oxLDL expression in human atherosclerotic arteries. Furthermore, elevated oxLDL levels were detected in the adventitial layer, where LV are primarily present. Treatment of human lymphatic endothelial cells (LEC) with oxLDL inhibited in vitro tube formation, while nLDL stimulated it. Similar results were observed with Matrigel plug assay in vivo. CD36 deletion in mice and its siRNA-mediated knockdown in LEC prevented oxLDL-induced inhibition of lymphangiogenesis. In addition, oxLDL via CD36 receptor suppressed cell cycle, downregulated AKT and eNOS expression, and increased levels of p27 in LEC. Collectively, these results indicate that oxLDL inhibits lymphangiogenesis via CD36-mediated regulation of AKT/eNOS pathway and cell cycle. These findings suggest that therapeutic blockade of LEC CD36 may promote arterial lymphangiogenesis, leading to increased cholesterol removal from the arterial wall and reduced atherosclerosis.

We present a novel, nonparametric form for compactly representing entangled many-body quantum states, which we call a “Gaussian process state.” In contrast to other approaches, we define this state explicitly in terms of a configurational data set, with the probability amplitudes statistically inferred from this data according to Bayesian statistics. In this way, the nonlocal physical correlated features of the state can be analytically resummed, allowing for exponential complexity to underpin the ansatz, but efficiently represented in a small data set. The state is found to be highly compact, systematically improvable, and efficient to sample, representing a large number of known variational states within its span. It is also proven to be a “universal approximator” for quantum states, able to capture any entangled many-body state with increasing data-set size. We develop two numerical approaches which can learn this form directly—a fragmentation approach and direct variational optimization—and apply these schemes to the fermionic Hubbard model. We find competitive or superior descriptions of correlated quantum problems compared to existing state-of-the-art variational ansatzes, as well as other numerical methods.

AimsMacropinocytosis is a major endocytic pathway by which dendritic cells (DCs) internalize antigens in the periphery. Despite the importance of DCs in the initiation and control of adaptive immune responses, the signaling mechanisms mediating DC macropinocytosis of antigens remain largely unknown. The goal of the present study was to investigate whether protein kinase C (PKC) is involved in stimulation of DC macropinocytosis and, if so, to identify the specific PKC isoform(s) and downstream signaling mechanisms involved.MethodsVarious cellular, molecular and immunological techniques, pharmacological approaches and genetic knockout mice were utilized to investigate the signaling mechanisms mediating DC macropinocytosis.ResultsConfocal laser scanning microscopy confirmed that DCs internalize fluorescent antigens (ovalbumin) using macropinocytosis. Pharmacological blockade of classical and novel PKC isoforms using calphostin C abolished both phorbol ester- and hepatocyte growth factor-induced antigen macropinocytosis in DCs. The qRT-PCR experiments identified PKCδ as the dominant PKC isoform in DCs. Genetic studies demonstrated the functional role of PKCδ in DC macropinocytosis of antigens, their subsequent maturation, and secretion of various T-cell stimulatory cytokines, including IL-1α, TNF-α and IFN-β. Additional mechanistic studies identified NADPH oxidase 2 (Nox2) and intracellular superoxide anion as important players in DC macropinocytosis of antigens downstream of PKCδ activation.ConclusionThe findings of the present study demonstrate a novel mechanism by which PKCδ activation via stimulation of Nox2 activity and downstream redox signaling promotes DC macropinocytosis of antigens. PKCδ/Nox2-mediated antigen macropinocytosis stimulates maturation of DCs and secretion of T-cell stimulatory cytokines. These findings may contribute to a better understanding of the regulatory mechanisms in DC macropinocytosis and downstream regulation of T-cell-mediated responses.

In the special issue “Oxidative Stress in Cardiovascular Disease” authors were invited to submit papers that investigate key questions in the field of cardiovascular free radical biology. The original research articles included in this issue provide important information regarding novel aspects of reactive oxygen species (ROS)-mediated signaling, which have important implications in physiological and pathophysiological cardiovascular processes. The issue also included a number of review articles that highlight areas of intense research in the fields of free radical biology and cardiovascular medicine.

The success of first-principles electronic-structure calculation for predictive modeling in chemistry, solid-state physics, and materials science is constrained by the limitations on simulated length scales and timescales due to the computational cost and its scaling. Techniques based on machine-learning ideas for interpolating the Born-Oppenheimer potential energy surface without explicitly describing electrons have recently shown great promise, but accurately and efficiently fitting the physically relevant space of configurations remains a challenging goal. Here, we present a Gaussian approximation potential for silicon that achieves this milestone, accurately reproducing density-functional-theory reference results for a wide range of observable properties, including crystal, liquid, and amorphous bulk phases, as well as point, line, and plane defects. We demonstrate that this new potential enables calculations such as finite-temperature phase-boundary lines, self-diffusivity in the liquid, formation of the amorphous by slow quench, and dynamic brittle fracture, all of which are very expensive with a first-principles method. We show that the uncertainty quantification inherent to the Gaussian process regression framework gives a qualitative estimate of the potential’s accuracy for a given atomic configuration. The success of this model shows that it is indeed possible to create a useful machine-learning-based interatomic potential that comprehensively describes a material on the atomic scale and serves as a template for the development of such models in the future.

A variety of vascular pathologies, including hypertension, restenosis and atherosclerosis, are characterized by vascular smooth muscle cell (VSMC) hypertrophy and migration. NADPH oxidase 1 (Nox1) plays a pivotal role in these phenotypes via distinct downstream signaling. However, the mediators differentiating these distinct phenotypes and their precise role in vascular disease are still not clear. The present study was designed to identify novel targets of VSMC Nox1 signaling using 2D Differential In-Gel Electrophoresis and Mass Spectrometry (2D-DIGE/MS). VSMC treatment with scrambled (Scrmb) or Nox1 siRNA and incubation with the oxidant hydrogen peroxide (H2O2; 50 µM, 3 h) followed by 2D-DIGE/MS on cell lysates identified 10 target proteins. Among these proteins, actin-related protein 2/3 complex subunit 2 (ARPC2) with no previous link to Nox isozymes, H2O2, or other reactive oxygen species (ROS), was identified and postulated to play an intermediary role in VSMC migration. Western blot confirmed that Nox1 mediates H2O2-induced ARPC2 expression in VSMC. Treatment with a p38 MAPK inhibitor (SB203580) resulted in reduced ARPC2 expression in H2O2-treated VSMC. Additionally, wound-healing “scratch” assay confirmed that H2O2 stimulates VSMC migration via Nox1. Importantly, gene silencing of ARPC2 suppressed H2O2-stimulated VSMC migration. These results demonstrate for the first time that Nox1-mediated VSMC migration involves ARPC2 as a downstream signaling target.

Although NADPH oxidase 4 (Nox4) is the most abundant Nox isoform in systemic vascular endothelial and smooth muscle cells, its function in the vascular tissue is not entirely known. The literature describes a pathophysiological role for Nox4 in cardiovascular disease; however, some studies have reported that it has a protective role. To date, specific Nox4 inhibitors are not available; hence, the development of a pharmacologic tool to assess Nox4’s pathophysiological role garners intense interest. In this study, we selected peptides corresponding to regions in the Nox4 oxidase complex critical to holoenzyme activity and postulated their utility as specific competitive inhibitors. Previous studies in our laboratory yielded selective inhibition of Nox2 using this strategy. We postulated that peptides mimicking the Nox4 B-loop and C-terminus and regions on p22phox inhibit Nox4 activity. To test our hypothesis, the inhibitory activity of Nox4 B-loop and C-terminal peptides as well as N-terminal p22phox peptides was assessed in a reconstituted Nox4 system. Our findings demonstrate that Nox4 inhibition is not achieved by preincubation with this comprehensive array of peptides derived from previously identified active regions. These findings suggest that Nox4 exists in a tightly assembled and active conformation which, unlike other Noxes, cannot be disrupted by conventional means.