An Intrinsically Disordered Peptide from Ebola Virus VP35 Controls Viral RNA Synthesis by Modulating Nucleoprotein-RNA Interactions
Abstract not provided
Leung, Daisy W.↗
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Targeting Metabolic Reprogramming by Influenza Infection for Therapeutic Intervention
Not Available
60 APPLIED LIFE SCIENCES↗
Cellular Senescence Is Induced by the Environmental Neurotoxin Paraquat and Contributes to Neuropathology Linked to Parkinson’s Disease
Exposure to the herbicide paraquat (PQ) is associated with an increased risk of idiopathic Parkinson’s disease (PD). Therapies based on PQ’s presumed mechanisms of action have not, however, yielded effective disease therapies. Cellular senescence is an anticancer mechanism that arrests proliferation of replication-competent cells and results in a pro-inflammatory senescence associated secretory phenotype (SASP) capable of damaging neighboring tissues. Here, we demonstrate that senescent cell markers are preferentially present within astrocytes in PD brain tissues. Additionally, PQ was found to induce astrocytic senescence and an SASP in vitro and in vivo, and senescent cell depletion in the latter protects against PQ-induced neuropathology. Our data suggest that exposure to certain environmental toxins promotes accumulation of senescent cells in the aging brain, which can contribute to dopaminergic neurodegeneration. Therapies that target senescent cells may constitute a strategy for treatment of sporadic PD, for which environmental exposure is a major risk factor.
59 BASIC BIOLOGICAL SCIENCES↗
The Influenza A Virus Endoribonuclease PA-X Usurps Host mRNA Processing Machinery to Limit Host Gene Expression
Often, many viruses shut off host gene expression to inhibit antiviral responses. Viral proteins and host proteins required for viral replication are typically spared in this process, yet the mechanisms of target selectivity during host shutoff remain poorly understood. Using transcriptome-wide and targeted reporter experiments, we demonstrate that the influenza A virus endoribonuclease PA-X usurps RNA splicing to selectively target host RNAs for destruction. Proximity-labeling proteomics reveals that PA-X interacts with cellular RNA processing proteins, some of which are partially required for host shutoff. Thus, PA-X taps into host nuclear pre-mRNA processing mechanisms to destroy nascent mRNAs shortly after their synthesis. This mechanism sets PA-X apart from other viral host shutoff proteins that target actively translating mRNAs in the cytoplasm. Our study reveals a unique mechanism of host shutoff that helps us understand how influenza viruses suppress host gene expression.
59 BASIC BIOLOGICAL SCIENCES↗
A Zika virus mutation enhances transmission potential and confers escape from protective dengue virus immunity
Zika virus (ZIKV) and dengue virus (DENV) are arthropod-borne pathogenic flaviviruses that co-circulate in many countries. To understand some of the pressures that influence ZIKV evolution, we mimic the natural transmission cycle by repeating serial passaging of ZIKV through cultured mosquito cells and either DENV-naive or DENV-immune mice. Compared with wild-type ZIKV, the strains passaged under both conditions exhibit increased pathogenesis in DENV-immune mice. Application of reverse genetics identifies an isoleucine-to-valine mutation (I39V) in the NS2B proteins of both passaged strains that confers enhanced fitness and escape from pre-existing DENV immunity. Introduction of I39V or I39T, a naturally occurring homologous mutation detected in recent ZIKV isolates, increases the replication of wild-type ZIKV in human neuronal precursor cells and laboratory-raised mosquitoes. Our data indicate that ZIKV strains with enhanced transmissibility and pathogenicity can emerge in DENV-naive or -immune settings, and that NS2B-I39 mutants may represent ZIKV variants of interest.
59 BASIC BIOLOGICAL SCIENCES↗
Zika-specific neutralizing antibodies targeting inter-dimer envelope epitopes
Zika virus (ZIKV) is an emerging pathogen that causes devastating congenital defects. The overlapping epidemiology and immunologic cross-reactivity between ZIKV and dengue virus (DENV) pose complex challenges to vaccine design, given the potential for antibody-dependent enhancement of disease. Therefore, classification of ZIKV-specific antibody targets is of notable value. From a ZIKV-infected rhesus macaque, we identify ZIKV-reactive B cells and isolate potent neutralizing monoclonal antibodies (mAbs) with no cross-reactivity to DENV. We group these mAbs into four distinct antigenic groups targeting ZIKV-specific cross-protomer epitopes on the envelope glycoprotein. Co-crystal structures of representative mAbs in complex with ZIKV envelope glycoprotein reveal envelope-dimer epitope and unique dimer-dimer epitope targeting. All four specificities are serologically identified in convalescent humans following ZIKV infection, and representative mAbs from all four groups protect against ZIKV replication in mice. These results provide key insights into ZIKV-specific antigenicity and have implications for ZIKV vaccine, diagnostic, and therapeutic development.
59 BASIC BIOLOGICAL SCIENCES↗
Comparative analysis of temperature dependent properties of commercial nuclear fuel pellet and surrogates undergoing cracking: A review
A variety of normal and accident scenarios can generate thermal stresses enough to cause cracking in light water reactor (LWR) fuel. To better understand this behavior, cracking experiments have been carried out to induce thermal gradients in fuel pellets via induction heating and direct resistance heating. In this study, Ceria (CeO 2 ) and yttria stabilized zirconia (YSZ) pellets were chosen as a surrogate fuel material for uranium dioxide (UO 2 ). Induction heating was done using copper coils and molybdenum susceptors which heated the surrogates to a threshold temperature that is sufficiently high for the fuel material to conduct current. Thereafter, direct resistance heating was achieved by passing current through the specimen using a DC power supply to introduce volumetric heating to replicate reactor operating conditions. Simultaneous real-time dual imaging of the ceria pellet surface has been designed using optical and infra-red camera system to capture images of cracks and full-field temperature gradients on pellet. It was observed that YSZ pellets need a much higher voltage-current for volumetric heating and do not exhibit the cracking pattern as ceria and UO 2 , hence YSZ was ruled out for further consideration. Ceria was found to be an appropriate surrogate for UO 2 . Furthermore, the experimental set up and test conditions demand the need to primarily understand the thermo-physical, mechanical and optical properties of UO 2 and the surrogates. Thus, it is essential to have an in-depth knowledge about the various temperature dependent properties of UO 2 and CeO 2 for establishing a comparative analysis between the surrogate and UO 2 for conducting experiments.
36 MATERIALS SCIENCE↗
The Controversial Role of Inter-diffusion in Glass Alteration
Current kinetic models for nuclear waste glasses (e.g. GM2001, GRAAL) are based on a set of mechanisms that have been generally agreed upon within the international waste glass community. These mechanisms are: hydration of the glass, ion exchange reactions (the two processes are referred as inter-diffusion), hydrolysis of the silicate network, and condensation/precipitation of partly or completely hydrolyzed species that produces a porous and amorphous layer and crystalline phases on surface of the altered glass. Recently, a new idea with origins in the mineral dissolution community has been proposed that excludes inter-diffusion process as a potential rate-limiting mechanism. To understand how the so-called interfacial dissolution/precipitation model can change the current understanding of glass behavior, a key experiment used to account for this model was replicated to further revisit the interpretation. This experiment was performed at 50°C, with SON68 glass, in static mode, deionized water and S/V ratio of 10 m-1 for 6 months. It turn out that glass alters in an intermediate kinetic regime between the forward and the residual rate. According to previous and new solid characterizations, it is concluded that neither a simple inter-diffusion model nor the interfacial dissolution precipitation model can account for the observed elemental profiles within the alteration layer. More generally, far and close-to-saturation conditions must be distinguished and literature provides evidences that inter-diffusion takes place in slightly acidic conditions and far from saturation. However, closer to saturation, when a sufficiently dense layer is formed, a new approach is proposed requiring a full description of chemical reactions taking place within the alteration layer and involving water molecules as it is thought that water accessibility to the pristine glass is the rate-limiting process.
Gin, Stephane↗
A Bioinspired Multicomponent Catalytic System for Converting Carbon Dioxide into Methanol Autocatalytically
Nature utilizes multicomponent catalyst systems to convert simple, abundant starting materials to complex molecules that are essential for life. In contrast, synthetic chemical transformations rarely adopt this strategy because the sophisticated supramolecular assemblies used by biology for active site separation and substrate trafficking are difficult to replicate. Herein, a method is described for multicomponent catalyst separation that involves encapsulating transition metal complexes in nanoporous materials called metal-organic frameworks. The multicomponent catalyst system was highly active for converting hydrogen and carbon dioxide to methanol, and it could be formulated so that it is readily recyclable. Moreover, an autocatalytic feature was uncovered that was only made possible by utilizing the multicomponent catalyst strategy employed. Furthermore, these results open avenues for obtaining fuel from abundant and renewable resources.
08 HYDROGEN↗
Agent-based simulation and child protection systems: Rationale, implementation, and verification
Simulation models are an important tool used in health care and other disciplines to support operational research and decision-making. In the child protection literature, simulation models are an under-utilized source of research evidence. Here, in this paper, we describe the rationale for and the development of an agent-based simulation of a child protection system in the US. Using the investigation, prevention service, and placement histories of 600,000 children served in an urban child welfare system, we walk the reader through the development of a prototype known as OSPEDALE. The governing equations built into OSPEDALE probabilistically simulate the onset of investigations. Then, drawing from empirical survival distributions, the governing equations trace the probability of subsequent interactions with the system (recurrence of maltreatment, service referrals, and placement) conditional on the characteristics of children, their assessed risk level, and prior child protection system involvement. As an initial test of OSPEDALE's utility, we compare empirical admission counts with counts generated from OSPEDALE. Though the verification step is admittedly simple, the comparison shows that OSPEDALE replicates the empirical count of new admissions closely enough to justify further investment in OSPEDALE. Management of public child protection systems is increasingly research evidence-dependent. The emphasis on research evidence as a decision-support tool has elevated evidence acquired through randomized clinical trials. Though important, the evidence from clinical trials represents only one type of research evidence. Properly specified, simulation models are another source of evidence with real-world relevance.
60 APPLIED LIFE SCIENCES↗
Trypanosoma brucei parasites occupy and functionally adapt to the adipose tissue in mice
Trypanosoma brucei is an extracellular parasite that causes sleeping sickness. In mammalian hosts, trypanosomes are thought to exist in two major niches: early in infection, they populate the blood; later, they breach the blood-brain barrier. Working with a well-established mouse model, we discovered that adipose tissue constitutes a third major reservoir for T. brucei . Parasites from adipose tissue, here termed adipose tissue forms (ATFs), can replicate and were capable of infecting a naive animal. ATFs were transcriptionally distinct from bloodstream forms, and the genes upregulated included putative fatty acid β-oxidation enzymes. Consistent with this, ATFs were able to utilize exogenous myristate and form β-oxidation intermediates, suggesting that ATF parasites can use fatty acids as an external carbon source. Lastly, these findings identify the adipose tissue as a niche for T. brucei during its mammalian life cycle and could potentially explain the weight loss associated with sleeping sickness.
59 BASIC BIOLOGICAL SCIENCES↗
Multi-platform ’Omics Analysis of Human Ebola Virus Disease Pathogenesis
The pathogenesis of human Ebola virus disease (EVD) is complex. EVD is characterized by high levels of virus replication and dissemination, dysregulated immune responses, extensive virus- and host-mediated tissue damage, and disordered coagulation. To clarify how host responses contribute to EVD pathophysiology, we performed multi-platform ’omics analysis of peripheral blood mononuclear cells and plasma from EVD patients. Our results indicate that EVD molecular signatures overlap with those of sepsis, imply that pancreatic enzymes contribute to tissue damage in fatal EVD, and suggest that Ebola virus infection may induce aberrant neutrophils whose activity could explain hallmarks of fatal EVD. Moreover, integrated biomarker prediction identified putative biomarkers from different data platforms that differentiated survivors and fatalities early after infection. We report this work reveals insight into EVD pathogenesis, suggests an effective approach for biomarker identification, and provides an important community resource for further analysis of human EVD severity.
59 BASIC BIOLOGICAL SCIENCES↗
Generative large language models for predictive maintenance planning
Maintenance planning and the generation of necessary components for tasks can prove time-consuming and complex. Automating the creation of recurring or similar tasks by leveraging previous planning packages and data, while uncovering insights to automate planning package generation, presents an opportunity to conserve valuable time and resources. This work aims to harness the textual and probabilistic capabilities of large language models (LLMs) to automate the generation of planning packages. Utilizing diverse data sources ranging from raw data to handwritten text, both singular and collaborative LLMs are trained and tested. Results demonstrate their capability to generate essential planning package components, effectively replicating the statistical patterns in the data. This demonstrates the use of these tools inside a digital asset for automated planning. This work outlines a methodology for constructing datasets, a training suite, and evaluation methods for LLM-based textual and conversational planning tools utilized in an asset digital twin. Results indicate that the fine-tuned models generate estimated planning information within the statistical ranges observed in real maintenance data. The models achieve high accuracy (>90%) in document question-answering and instruction generation tasks. Furthermore, the conversational retrieval-augmented generation (RAG) assistant system achieves 100% document retrieval accuracy, while conversational information capture exceeds 98% across the majority of work-package assistant modules.
97 MATHEMATICS AND COMPUTING↗
An updated Lagrangian LBM–DEM–FEM coupling model for dual-permeability fissured porous media with embedded discontinuities
Many engineering applications and geological processes involve embedded discontinuities in porous media across multiple length scales (e.g. rock joints, grain boundaries, deformation bands and faults). Understanding the multiscale path-dependent hydro-mechanical responses of these interfaces across length scales is of ultimate importance for applications such as CO 2 sequestration, hydraulic fracture and earthquake rupture dynamics. While there exist mathematical frameworks such as extended finite element and assumed strain to replicate the kinematics of the interfaces, modeling the cyclic hydro-mechanical constitutive responses of the interfaces remains a difficult task. Here this paper presents a semi-data-driven multiscale approach that obtains both the traction-separation law and the aperture-porosity–permeability relation from micro-mechanical simulations performed on representative elementary volumes in the finite deformation range. To speed up multiscale simulations, the incremental constitutive updates of the mechanical responses are obtained from discrete element simulations at the representative elementary volume whereas the hydraulic responses are generated from a neural network trained with data from lattice Boltzmann simulations. These responses are then linked to a macroscopic dual-permeability model. This approach allows one to bypass the need of deriving multi-physical phenomenological laws for complex loading paths. More importantly, it enables the capturing of the evolving anisotropy of the permeabilities of the macro- and micro-pores. A set of numerical experiments are used to demonstrate the robustness of the proposed model.
42 ENGINEERING↗
SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials
This work examines the frame-invariance (and the lack thereof) exhibited in simulated anisotropic elasto-plastic responses generated from supervised machine learning of classical multi-layer and informed-graph-based neural networks, and proposes different remedies to fix this drawback. The inherent hierarchical relations among physical quantities and state variables in an elasto-plasticity model are first represented as informed, directed graphs, where three variations of the graph are tested. While feed-forward neural networks are used to train path-independent constitutive relations (e.g., elasticity), recurrent neural networks are used to replicate responses that depends on the deformation history, i.e. or path dependent. In dealing with the objectivity deficiency, we use the spectral form to represent tensors and, subsequently, three metrics, the Euclidean distance between the Euler Angles, the distance from the identity matrix, and geodesic on the unit sphere in Lie algebra, can be employed to constitute objective functions for the supervised machine learning. In this, the aim is to minimize the measured distance between the true and the predicted 3D rotation entities. Following this, we conduct numerical experiments on how these metrics, which are theoretically equivalent, may lead to differences in the efficiency of the supervised machine learning as well as the accuracy and robustness of the resultant models. Neural network models trained with tensors represented in component form for a given Cartesian coordinate system are used as a benchmark. Our numerical tests show that, even given the same amount of information and data, the quality of the anisotropic elasto-plasticity model is highly sensitive to the way tensors are represented and measured. The results reveal that using a loss function based on geodesic on the unit sphere in Lie algebra together with an informed, directed graph yield significantly more accurate rotation prediction than the other tested approaches.
42 ENGINEERING↗
Tonal Emergence: An agent-based model of tonal coordination
Humans have a remarkable capacity for coordination. Our ability to interact and act jointly in groups is crucial to our success as a species. Joint Action (JA) research has often concerned itself with simplistic behaviors in highly constrained laboratory tasks. But there has been a growing interest in understanding complex coordination in more open-ended contexts. In this regard, collective music improvisation has emerged as a fascinating model domain for studying basic JA mechanisms in an unconstrained and highly sophisticated setting. A number of empirical studies have begun to elucidate coordination mechanisms underlying joint musical improvisation, but these empirical findings have yet to be cached out in a working computational model. The present work fills this gap by presenting TonalEmergence, an idealized agent-based model of improvised musical coordination. TonalEmergence models the coordination of notes played by improvisers to generate harmony (i.e., tonality), by simulating agents that stochastically generate notes biased towards maximizing harmonic consonance given their partner’s previous notes. Here, the model replicates an interesting empirical result from a previous study of professional jazz pianists: feedback loops of mutual adaptation between interacting agents support the production of consonant harmony. The model is further explored to show how complex tonal dynamics, such as the production and dissolution of stable tonal centers, are supported by agents that are characterized by (i) a tendency to strive toward consonance, (ii) stochasticity, and (iii) a limited memory for previously played notes. TonalEmergence thus provides a grounded computational model to simulate and probe the coordination mechanisms underpinning one of the more remarkable feats of human cognition: collective music improvisation.
60 APPLIED LIFE SCIENCES↗
Study of soot production for double injections of n-dodecane in CI engine-like conditions
Not Available
Moiz, Ahmed Abdul↗
Bayesian prior construction for uncertainty quantification in first-principles statistical mechanics
First-principles statistical mechanics enables the prediction of thermodynamic and kinetic properties of materials, but is computationally expensive. Many approaches require surrogate models to calculate energies within Monte Carlo or molecular dynamics simulations. Inexpensive surrogates such as cluster expansions enable otherwise intractable calculations by interpolating data from higher accuracy methods, such as Density Functional Theory (DFT). Surrogate models introduce uncertainty into downstream calculations, in addition to any uncertainty inherent to DFT calculations. Bayesian frameworks address this by quantifying uncertainty and incorporating expert knowledge through priors. However, constructing effective priors remains challenging. This work introduces and describes practical strategies for building Bayesian cluster expansions, focusing on basis truncation, hyperparameter selection, and ground state replication. We analyze multiple basis truncation schemes, compare cross-validation to the evidence-approximation for hyperparameter optimization, and provide methods to find and enforce ground-state-preserving models through priors. Additionally, we compare the uncertainties between different approximations to DFT (LDA, PBE, SCAN) against the uncertainty introduced with the use of cluster expansion surrogate models. These approaches are demonstrated on the BCC Li x Mg 1-x and Li x Al 1-x alloys, which are both of interest for solid-state Li batteries. Our results provide guidelines for constructing and utilizing Bayesian cluster expansions, thereby improving the transparency of materials modeling. Furthermore, the approaches and insights developed in this work can be transferred to a wide range of cluster expansion surrogate models, including the atomic cluster expansion and related machine-learned interatomic potential architectures.
Alloy theory↗