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The role of Liouville operators in the study of dynamical systems through the use of occupation measures has been an active area of research in control theory over the past decade. This article investigates Liouville operators over the Hardy space, which encode complex ordinary differential equations in an operator over a reproducing kernel Hilbert space.
Malaria vaccine candidate RTS,S/AS01 is based on the central and C-terminal regions of the circumsporozoite protein (CSP) of P. falciparum. mAb397 was isolated from a volunteer in an RTS,S/AS01 clinical trial, and it protects mice from infection by malaria sporozoites. However, mAb397 originates from the less commonly used VH3-15 germline gene compared to the VH3-30/33 antibodies generally elicited by RTS,S to the central NANP repeat region of CSP. The crystal structure of mAb397 with an NPNA4 peptide shows that the central NPNA forms a type I β-turn and is the main recognition motif. In most anti-NANP antibodies studied to date, a germline-encoded Trp is used to engage the Pro in NPNA β-turns, but here the Trp interacts with the first Asn. This “conserved” Trp, however, can arise from different germline genes and be located in the heavy or the light chain. Variation in the terminal ψ angles of the NPNA β-turns results in different dispositions of the subsequent NPNA and, hence, different stoichiometries and modes of antibody binding to rsCSP. Diverse protective antibodies against NANP repeats are therefore not limited to a single germline gene response or mode of binding.
Intrinsically disordered proteins/regions (IDPRs) are a very large and functionally important class of proteins that participate in weak multivalent interactions in protein complexes. They are recalcitrant for interrogations using X-ray crystallography and cryo-EM. The IDPRs observed at the interface of the photosynthetic pigment protein complexes (PPCs) remain much less clear, e.g., the major cyanobacterial light-harvesting complex (PBS) contains an unstructured PB-loop insertion in the phycocyanobilin domain (PB domain) of ApcE (the largest polypeptide in PBS). Here, a joint platform is built to probe such structural domains. This platform is characterized by two-round progressive justifications of in silico models by using the structural mass spectrometry data. First, the AlphaFold-generated 3D structure of the PB domain (containing PB-loop) was justified in the context of PBS. Second, docking the AlphaFold-generated ApcG (a ligand) into the first-step justified structure (a receptor). The final ligand-receptor complex was then subjected to a second-round justification, again, by using unequivocal isotopically-encoded cross-links identified in LC-MS/MS. This work reveals a full-length PB-loop structure modelled in the PBS basal cylinder, free from any spatial conflicts against the other subunits in PBS. The structure of PB domain highlights the close associations of the intrinsically disordered PB-loop with its binding partners in PBS, including ApcG, another IDPR. The PB-loop region involved in the binding of photosystem II (PSII) is also discussed in the context of excitation energy transfer regulation. Finally, this work calls attention to the highly disordered, yet interrogatable interface between the light-harvesting antenna complexes and the reaction centers.
A simple spatial filter for 2D projection MR imaging is introduced. It works in the third (unresolved) direction to eliminate uniform or slowly varying interfering background signals. A constant amplitude gradient pulse in the unresolved direction is applied at the same time as the usual phase encode gradient during 2D acquisition. The filter is demonstrated for root imaging in soil, where background soil water signals can be troublesome. The filter suppresses the soil water signal while preserving the desired signal of plant roots. Fundamental to the operation of the filter is that the roots are sparse in the image domain, meaning there are relatively few pixels with multiple roots present. Here, the performance of the through-plane filter is demonstrated and compares favorably to more conventional in-plane spatial filtering.
Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.
We present a novel unsupervised machine learning method for quantitative representation of scanning electron micrographs and its applications and performance for nuclear forensic analysis of uranium ore concentrates. The method uses a vector quantizing variational autoencoder followed by a histogram operation to encode a micrograph into a single dimensional representation, called the latent vector. The method requires no extant labeling of the data and can be applied over large datasets of micrographs with minimal human interaction. The representations generated are broadly descriptive of each micrograph and the microstructure of the material imaged. In the case of uranium ore concentrate analysis, the representations were amenable to processing reagent and ore concentrate species classification with accuracy of 81:8%, which is competitive with state-of-the-art supervised networks. The representations were also used to classify previously unseen processing routes, were able to classify imaging parameters such as magnification (to 76:0% accuracy), were able to classify fine grained process parameters such as calcining temperature (to 74:4% accuracy), and their informatic properties indicate that they are generally descriptive of the image represented. This method can be applied across microstructure analysis fields to perform quantitative analysis without the need for labor intensive and possibly biased human analysis.
Cyclopropane-functionalized hydrocarbons are excellent fuels due their high energy density. However, the organic synthesis of these molecules is challenging and harmful to the environment. In this work we produced polycyclopropanated fatty acids in bacteria. These molecules can be easily converted into renewable fuels for high energy applications such as shipping, long-haul transport, aviation, and rocketry. We explored the chemical diversity encoded in the genome of thousands of bacteria to identify and repurpose naturally occurring cyclopropanated molecules. We identified a set of candidate iterative polyketide synthases (iPKSs) predicted to produce polycyclopropanated fatty acids (POP-FAs), expressed these PKSs in Streptomyces coelicolor and produced the POP-FAs. We determined the structure of the molecules and increased their production 22-fold. Polycyclopropanated fatty acid methyl esters (POP-FAMEs) were obtained by methyl esterifying the POP-FAs. Finally, we calculated that our POP fuel candidates can have energy densities of more than 50 MJ/L. Our research shows that POP-FAMEs and other polyketide derived POPs have the energetic properties for energy-demanding applications for which sustainable alternatives are scarce.
Diverse usage patterns induce complex and variable aging behaviors in lithiumion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery’s current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery’s degradation trajectory, are time-intensive, and cannot be practically performed in onboard applications. Here, in this work, we leverage portions of operational measurements in combination with an interpretable machine learning model to enable rapid, onboard battery health diagnostics and prognostics without offline diagnostic testing and the requirement of historical data. We integrate mechanistic constraints within an encoder-decoder architecture to extract electrode states in a physically interpretable latent space and enable improved reconstruction of the degradation path. The health diagnosis model framework can be flexibly applied across diverse application interests with slight fine-tuning.
Rendezvous algorithms encode a communication pattern that is useful when processors sending data do not know who the receiving processors should be, or vice versa. The idea is to define an intermediate decomposition where datums from different sending processors can ”rendezvous” to perform a computation, in a manner that both the senders and eventual receivers of the results can identify the appropriate rendezvous processor. Though they were originally designed for interpolating between overlaid grids with independent parallel decompositions (Plimpton et al., 2004), we have recently found rendezvous algorithms useful for a variety of operations in particle- or grid-based simulation codes when running large problems on large numbers of processors. In particular, we show they can perform well when a load-balanced intermediate decomposition is randomized and not spatial, requiring all-to-all communication to move data between processors. In this case rendezvous algorithms leverage the large bisection communication bandwidths which parallel machines provide. We describe how rendezvous algorithms work in a scientific computing context and give specific examples for molecular dynamics and Direct Simulation Monte Carlo codes which result in dramatic performance improvements versus simpler algorithms which do not scale as well. We explain how a generic rendezvous algorithm can be implemented, and also point out similarities with the MapReduce paradigm popularized by Google and Hadoop.
Here, we show that the universal scaling properties of the chiral phase transition in quantum chromodynamics (QCD) at the macroscale are encoded in the microscopic energy levels of the quarks in the background of gluon fields, defined as eigenvalues of the Dirac operator. We introduce a novel relation between the cumulants of the chiral condensate, and the correlations among the energy levels of quarks, i.e., the eigenspectra of the massless QCD Dirac operator. This relation elucidates how the fluctuations of the chiral condensate arise from the correlations within the infrared part of the energy spectra of quarks, and naturally leads to a generalization of the Banks-Casher relation for the cumulants of the chiral condensate. Then, through (2+1)-flavor lattice QCD calculations using HISQ action with varying light quark masses around the chiral phase transition temperature, we demonstrate that the correlations among the infrared part of the Dirac eigenvalue spectra exhibit same universal scaling behaviors as expected of the cumulants of the chiral condensate. We find that these universal scaling behaviors extend up to the physical values of the up and down quark masses.
Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.
We report the emergence of two-dimensional (2D) materials has captured the imagination of researchers since graphene was first exfoliated from graphite in 2004. Their exotic properties give rise to many exciting potential applications in advanced electronic, optoelectronic, energy and biomedical technologies. Scalable growth of high quality 2D materials is crucial for their adoption in technological applications the same way the arrival of high quality silicon single crystals was to the semiconductor industry. A huge amount of effort has been devoted to grow large-area, highly crystalline 2D crystals such as graphene and transition metal dichalcogenides (TMDs) through various methods. While CVD growth of wafer-scale monolayer graphene and TMDs has been demonstrated, considerable challenges still remain. In this perspective, we advocate for the focus on the crystal growth morphology as an underpinning for understanding, diagnosing and controlling the CVD process and environment for 2D material growth. Like snowflakes in nature, 2D crystals exhibit a rich variety of morphologies under different growth conditions. The mapping of crystal shapes in the growth parameter space “encodes” a wealth of information, the deciphering of which will lead to better understanding of the fundamental growth mechanism and materials properties. To this end, we envision a collective effort by the 2D materials community to establish the correlation between crystal shapes and the intrinsic thermodynamic and kinetic parameters for CVD reactions through integrated crystal growth experiment, database development and machine learning assisted predictive modeling, which will pave a robust path towards controlled synthesis of 2D materials and heterostructures.
While machine learning has found increasing use in predicting the properties of polymeric materials with only a knowledge of chain architecture, determining the molecular factors underpinning properties (“interpretable AI”) has remained less well explored. We show that encoding chain chemistry in commonly employed formats, e.g., binary-valued fingerprints, leads to uniqueness issues during the hashing process to save storage space. This is because the hashing algorithm can map several chemical moieties into the same bit. These issues carry over into the ML algorithms, especially for “inverse” design and interpretable AI, and cannot be avoided by changing the length of the fingerprint. Using MACCS key featurizations of monomer repeats resolves some of these issues, and we show that a few substructures consistently appear in top features for maximizing permeability across several gases and ML models. These are carbon–carbon double bonds (as in polyacetylenes) especially when they are associated with methyl groups (found in branching architectures). Here these results, derived from the limited data set of ~ 500 polymers with experimental gas permeation data, are in agreement with physical insight and thus provide a robust foundation which could further enable study of these material classes through detailed experiments and simulations.
Bacterial microcompartments (BMCs) are self-assembling prokaryotic organelles which encapsulate enzymes within a polyhedral protein shell. Additionally, the shells are comprised of only two structural modules, distinct domains that form pentagonal and hexagonal building blocks, which occupy the vertices and facets, respectively. As all BMC loci encode at least one hexamer-forming and one pentamer-forming protein, the evolutionary history of BMCs can be interrogated from the perspective of their shells. Here, we discuss how structures of intact shells and detailed phylogenies of their building blocks from a recent phylogenomic survey distinguish families of these domains and reveal clade-specific structural features. These features suggest distinct functional roles that recur across diverse BMCs. For example, it is clear that carboxysomes independently arose twice from metabolosomes, yet the principles of shell assembly are remarkably conserved.
Accurate micromechanical simulation of polycrystalline materials requires a realistic digital representation of the grain scale microstructure. Here, this work demonstrates the use of a generative diffusion probabilistic model for synthesizing single phase polycrystalline realizations. The model performs well and is capable of producing realistic microstructures consisting of not just simple equiaxed structures but also structures exhibiting more complex spatial arrangements. Masked microstructure generation reveals that the model is context aware of morphological descriptors which may be encoded in the latent space. Training on more diverse data sets, with scaled up architectures, may enable development of future models capable of synthesizing even more complex microstructural features.
In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.
Dynamic graph embedding has emerged as a very effective technique for addressing diverse temporal graph analytic tasks (i.e., link prediction, node classification, recommender systems, anomaly detection, and graph generation) in various applications. Such temporal graphs exhibit heterogeneous transient dynamics, varying time intervals, and highly evolving node features throughout their evolution. Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics. In this paper, we develop a graph embedding model with uncertainty quantification, TransformerG2G, by exploiting the advanced transformer encoder to first learn intermediate node representations from its current state (t) and previous context (over timestamps [t–1,t–l], l is the length of context). Moreover, we employ two projection layers to generate lower-dimensional multivariate Gaussian distributions as each node's latent embedding at timestamp t. We consider diverse benchmarks with varying levels of "novelty" as measured by the TEA (Temporal Edge Appearance) plots. Here, our experiments demonstrate that the proposed TransformerG2G model outperforms conventional multi-step methods and our prior work (DynG2G) in terms of both link prediction accuracy and computational efficiency, especially for high degree of novelty. Furthermore, the learned time-dependent attention weights across multiple graph snapshots reveal the development of an automatic adaptive time stepping enabled by the transformer. Importantly, by examining the attention weights, we can uncover temporal dependencies, identify influential elements, and gain insights into the complex interactions within the graph structure. For example, we identified a strong correlation between attention weights and node degree at the various stages of the graph topology evolution.
SNARE (soluble N-ethylmaleimide sensitive factor attachment protein receptor) complex, composed of synaptobrevin, syntaxin, and SNAP25, forms the essential fusion machinery for neurotransmitter release. Recent studies have reported several mutations in the gene encoding SNAP25 as a causative factor for developmental and epileptic encephalopathies of infancy and childhood with diverse clinical manifestations. However, it remains unclear how SNAP25 mutations give rise to these disorders. In this work, we show that although structurally clustered mutations in SNAP25 give rise to related synaptic transmission phenotypes, specific alterations in spontaneous neurotransmitter release are a key factor to account for disease heterogeneity. Importantly, we identified a single mutation that augments spontaneous release without altering evoked release, suggesting that aberrant spontaneous release is sufficient to cause disease in humans.