Data analytics-guided rational design of antimicrobial nanomedicines against opportunistic, resistant pathogens.

Nanoparticle carriers can improve antibiotic efficacy by altering drug biodistribution. However, traditional screening is impracticable due to a massive dataspace. A hybrid informatics approach was developed to identify polymer, antibiotic, and particle determinants of antimicrobial nanomedicine activity against Burkholderia cepacia, and to model nanomedicine performance. Polymer glass transition temperature, drug octanol-water partition coefficient, strongest acid dissociation constant, physiological charge, particle diameter, count and mass mean polydispersity index, zeta potential, fraction drug released at 2 h, and fraction release slope at 2 h were highly correlated with antimicrobial performance. Graph analysis provided dimensionality reduction while preserving nonlinear descriptor-property relationships, enabling accurate modeling of nanomedicine performance. The model successfully predicted particle performance in holdout validation, with moderate accuracy at rank-ordering. This data analytics-guided approach provides an important step toward the development of a rational design framework for antimicrobial nanomedicines against resistant infections by selecting appropriate carriers and payloads for improved potency.

Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors.

This paper aims to identify structural motifs within a molecule that contribute the most toward a chemical being an endocrine disruptor. We have developed a deep neural network-based toolkit toward this aim. The trained model can virtually assess a synthetic chemical's potential to be an endocrine disruptor using machine-readable molecular representation, simplified molecular input line entry system (SMILES). Our proposed toolkit is a multilabel or multioutput classification model that combines both convolution and long short-term memory (LSTM) architectures. The toolkit leverages the advantages of an active learning-based framework that combines multiple sources of data. Class activation maps (CAMs) generated from the feature-extraction layers can identify the structural alerts and the chemical environment that determines the specificity of the structural alerts.

Quantum signatures for screening metavalent solids.

The objective of this paper is to describe a new data-driven framework for computational screening and discovery of a class of materials termed "metavalent" solids. "Metavalent" solids possess characteristics that are nominally associated with metallic and covalent bonding (in terms of conductivity and coordination numbers) but are distinctly different from both because they show anomalously large response properties and a unique bond-breaking mechanism that is not observed in either covalent or metallic solids. The paper introduces the use of Hirshfeld surface analysis to provide quantum level descriptors that can be used for rapid screening of crystallographic data to identify potentially new "metavalent" solids with novel and emergent properties.

Data Analytics Approach for Rational Design of Nanomedicines with Programmable Drug Release.

Drug delivery vehicles can improve the functional efficacy of existing antimicrobial therapies by improving biodistribution and targeting. A critical property of such nanomedicine formulations is their ability to control the release kinetics of their payloads. The combination of (and interactions among) polymer, drug, and nanoparticle properties gives rise to nonlinear behavioral relationships and large data space. These factors complicate both first-principles modeling and screening of nanomedicine formulations. Predictive analytics may offer a more efficient approach toward the rational design of nanomedicines by identifying key descriptors and correlating them to nanoparticle release behavior. In this work, antibiotic release kinetics data were generated from polyanhydride nanoparticle formulations with varying copolymer compositions, encapsulated drug type, and drug loading. Four antibiotics, doxycycline, rifampicin, chloramphenicol, and pyrazinamide, were used. Linear manifold learning methods were used to relate drug release properties with polymer, drug, and nanoparticle properties, and key descriptors were identified that are highly correlated with release properties. However, these linear methods could not predict release behavior. Nonlinear multivariate modeling based on graph theory was then used to deconvolute the governing relationships between these properties, and predictive models were generated to rapidly screen lead nanomedicine formulations with desirable release properties with minimal nanoparticle characterization. Release kinetics predictions of two drugs containing atoms not included in the model showed good agreement with experimental results, validating the model and indicating its potential to virtually explore new polymer and drug pairs not included in the training data set. The models were shown to be robust after the inclusion of these new formulations, in that the new inclusions did not significantly change model regression. This approach provides the first step toward the development of a framework that can be used to rationally design nanomedicine formulations by selecting the appropriate carrier for a drug payload to program desirable release kinetics.

Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing.

A recent trend in the development of high mass consumption electron devices is towards electronic textiles (e-textiles), smart wearable devices, smart clothes, and flexible or printable electronics. Intrinsically soft, stretchable, flexible, Wearable Memories and Computing devices (WMCs) bring us closer to sci-fi scenarios, where future electronic systems are totally integrated in our everyday outfits and help us in achieving a higher comfort level, interacting for us with other digital devices such as smartphones and domotics, or with analog devices, such as our brain/peripheral nervous system. WMC will enable each of us to contribute to open and big data systems as individual nodes, providing real-time information about physical and environmental parameters (including air pollution monitoring, sound and light pollution, chemical or radioactive fallout alert, network availability, and so on). Furthermore, WMC could be directly connected to human brain and enable extremely fast operation and unprecedented interface complexity, directly mapping the continuous states available to biological systems. This review focuses on recent advances in nanotechnology and materials science and pays particular attention to any result and promising technology to enable intrinsically soft, stretchable, flexible WMC.

Informatics derived materials databases for multifunctional properties.

In this review, we provide an overview of the development of quantitative structure-property relationships incorporating the impact of data uncertainty from small, limited knowledge data sets from which we rapidly develop new and larger databases. Unlike traditional database development, this informatics based approach is concurrent with the identification and discovery of the key metrics controlling structure-property relationships; and even more importantly we are now in a position to build materials databases based on design 'intent' and not just design parameters. This permits for example to establish materials databases that can be used for targeted multifunctional properties and not just one characteristic at a time as is presently done. This review provides a summary of the computational logic of building such virtual databases and gives some examples in the field of complex inorganic solids for scintillator applications.

Informatics guided discovery of surface structure-chemistry relationships in catalytic nanoparticles.

A data driven discovery strategy based on statistical learning principles is used to discover new correlations between electronic structure and catalytic activity of metal surfaces. From the quantitative formulations derived from this informatics based model, a high throughput computational framework for predicting binding energy as a function of surface chemistry and adsorption configuration that bypasses the need for repeated electronic structure calculations has been developed.

An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening.

Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS-Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform's classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS. In addition, the platform incorporates deep unsupervised learning models to visualize the chemical space of PFAS by clustering similar structures and linking related classes. Through real-world use cases, we demonstrate that PFAS-Atlas can rapidly screen for relationships between chemical structure and persistence, bioaccumulation, or toxicity data for PFAS. The platform can also guide the planning of the PFAS testing strategy by showing which PFAS classes urgently require further attention. Ultimately, the release of PFAS-Atlas will benefit both the PFAS research and regulation communities.

In situ Synchrotron X-ray Metrology Boosted by Automated Data Analysis for Real-time Monitoring of Cathode Calcination.

Synchrotron X-ray-based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on-site, on-the-fly analysis needed for real-time process monitoring. Here, a weighted lagged cross-correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X-ray diffraction metrology to monitor the calcination process of the archetypal nickel-based cathode, LiNiO2. The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO2, providing information for agile on-site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early-stage phase nucleation and growth. Expanding this data-powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.

Atomic-scale tomography: a 2020 vision.

Atomic-scale tomography (AST) is defined and its place in microscopy is considered. Arguments are made that AST, as defined, would be the ultimate microscopy. The available pathways for achieving AST are examined and we conclude that atom probe tomography (APT) may be a viable basis for AST on its own and that APT in conjunction with transmission electron microscopy is a likely path as well. Some possible configurations of instrumentation for achieving AST are described. The concept of metaimages is introduced where data from multiple techniques are melded to create synergies in a multidimensional data structure. When coupled with integrated computational materials engineering, structure-properties microscopy is envisioned. The implications of AST for science and technology are explored.

Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis.

Vibrational spectroscopy is a nondestructive technique commonly used in chemical and physical analyses to determine atomic structures and associated properties. However, the evaluation and interpretation of spectroscopic profiles based on human-identifiable peaks can be difficult and convoluted. To address this challenge, we present a reliable protocol based on supervised manifold learning techniques meant to connect vibrational spectra to a variety of complex and diverse atomic structure configurations. As an illustration, we examined a large database of virtual vibrational spectroscopy profiles generated from atomistic simulations for silicon structures subjected to different stress, amorphization, and disordering states. We evaluated representative features in those spectra via various linear and nonlinear dimensionality reduction techniques and used the reduced representation of those features with decision trees to correlate them with structural information unavailable through classical human-identifiable peak analysis. We show that our trained model accurately (over 97% accuracy) and robustly (insensitive to noise) disentangles the contribution from the different material states, hence demonstrating a comprehensive decoding of spectroscopic profiles beyond classical (human-identifiable) peak analysis.

Information-theoretic approach for the discovery of design rules for crystal chemistry.

In this work, it is shown that for the first time that, using information-entropy-based methods, one can quantitatively explore the relative impact of a wide multidimensional array of electronic and chemical bonding parameters on the structural stability of intermetallic compounds. Using an inorganic AB2 compound database as a template data platform, the evolution of design rules for crystal chemistry based on an information-theoretic partitioning classifier for a high-dimensional manifold of crystal chemistry descriptors is monitored. An application of this data-mining approach to establish chemical and structural design rules for crystal chemistry is demonstrated by showing that, when coupled with first-principles calculations, statistical inference methods can serve as a tool for significantly accelerating the prediction of unknown crystal structures.

Structure maps for A(I)4A(II)6(BO4)6X2 apatite compounds via data mining.

This paper describes a method to identify key crystallographic parameters that can serve as strong classifiers of crystal chemistries and hence define new structure maps. The selection of this pair of key parameters from a large set of potential classifiers is accomplished through a linear data-dimensionality reduction method. A multivariate data set of known A(I)(4)A(II)(6)(BO(4))(6)X(2) apatites is used as the basis for the study where each A(I)(4)A(II)(6)(BO(4))(6)X(2) compound is represented as a 29-dimensional vector, where the vector components are discrete scalar descriptors of electronic and crystal structure attributes. A new structure map, defined using the two distortion angles α(AII) (rotation angle of A(II)-A(II)-A(II) triangular units) and ψ(AIz = 0)(AI-O1) (angle the A(I)-O1 bond makes with the c axis when z = 0 for the A(I) site), is shown to classify apatite crystal chemistries based on site occupancy on the A, B and X sites. The classification is accomplished using a K-means clustering analysis.

Refining spatial distribution maps for atom probe tomography via data dimensionality reduction methods.

A mathematical framework based on singular value decomposition is used to analyze the covariance among interatomic frequency distributions in spatial distribution maps (SDMs). Using this approach, singular vectors that capture the covariance within the SDM data are obtained. The structurally relevant singular vectors (SRSVs) are identified. Using the SRSVs, we extract information from z-SDMs that not only captures the offset between the atomic planes but also captures the covariance in the atomic structure among the neighborhood atomic planes. These refined z-SDMs classify the Δ(Δz) slices in the SDMs into structurally relevant information, noise, and aberrations. The SRSVs are used to construct refined xy-SDMs that provide enhanced structural information for three-dimensional atom probe tomography.

Arterial and venous thrombotic stroke after ChAdOx1 nCoV-19 vaccine.

Vaccine-induced thrombosis with thrombocytopenia (VITT) is a recently-described condition associated with arterial and venous thrombosis following vaccination with the ChAdOx1 nCoV-19 (AstraZeneca) vaccine. This report describes two cases of stroke caused by arterial and venous thromboses presenting within 28 days of receiving the AstraZeneca vaccine. The patients were otherwise young and healthy with minimal risk factors for thrombosis yet developed a rapid, ultimately fatal neurological deterioration.The patients were significantly thrombocytopenic with disproportionately raised D-dimers, both of which are widely reported in this condition. Both cases had measurable immunoglobulin G platelet factor-4 antibodies detected via enzyme-linked immunosorbent assay, similar to those described in heparin-induced thrombocytopenia.These cases illustrate that physicians should be especially mindful of VITT in the context of evolving evidence on treatment and in view of the potentially rapid and catastrophic neurological deterioration, leading to fatality despite best supportive care.

RNA Nanovaccine Protects against White Spot Syndrome Virus in Shrimp.

In the last 15 years, crustacean fisheries have experienced billions of dollars in economic losses, primarily due to viral diseases caused by such pathogens as white spot syndrome virus (WSSV) in the Pacific white shrimp Litopenaeus vannamei and Asian tiger shrimp Penaeus monodon. To date, no effective measures are available to prevent or control disease outbreaks in these animals, despite their economic importance. Recently, double-stranded RNA-based vaccines have been shown to provide specific and robust protection against WSSV infection in cultured shrimp. However, the limited stability of double-stranded RNA is the most significant hurdle for the field application of these vaccines with respect to delivery within an aquatic system. Polyanhydride nanoparticles have been successfully used for the encapsulation and release of vaccine antigens. We have developed a double-stranded RNA-based nanovaccine for use in shrimp disease control with emphasis on the Pacific white shrimp L. vannamei. Nanoparticles based on copolymers of sebacic acid, 1,6-bis(p-carboxyphenoxy)hexane, and 1,8-bis(p-carboxyphenoxy)-3,6-dioxaoctane exhibited excellent safety profiles, as measured by shrimp survival and histological evaluation. Furthermore, the nanoparticles localized to tissue target replication sites for WSSV and persisted through 28 days postadministration. Finally, the nanovaccine provided ~80% protection in a lethal WSSV challenge model. This study demonstrates the exciting potential of a safe, effective, and field-applicable RNA nanovaccine that can be rationally designed against infectious diseases affecting aquaculture.

A database framework for rapid screening of structure-function relationships in PFAS chemistry.

This paper describes a database framework that enables one to rapidly explore systematics in structure-function relationships associated with new and emerging PFAS chemistries. The data framework maps high dimensional information associated with the SMILES approach of encoding molecular structure with functionality data including bioactivity and physicochemical property. This 'PFAS-Map' is a 3-dimensional unsupervised visualization tool that can automatically classify new PFAS chemistries based on current PFAS classification criteria. We provide examples on how the PFAS-Map can be utilized, including the prediction and estimation of yet unmeasured fundamental physical properties of PFAS chemistries, uncovering hierarchical characteristics in existing classification schemes, and the fusion of data from diverse sources.

Classification of apatite structures via topological data analysis: a framework for a 'Materials Barcode' representation of structure maps.

This paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new "Materials Barcode" schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds.

Tracking chemical processing pathways in combinatorial polymer libraries via data mining.

Changes in the molecular structure and composition of interpenetrating polymer networks (IPNs) can be used to tailor their properties. While the properties of IPNs are typically different than polymer blends, a clear understanding of the impact of changing polymerization sequence on the physical properties and the corresponding molecular bonding is needed. To address this issue, a data mining approach is used to identify the change with polymerization sequence of tensile and rheological properties of acrylate-epoxy IPNs. The experimental approach used to study the molecular structure is high throughput Fourier transform infrared (FTIR) spectroscopy. Analysis of the FTIR spectra of IPNs synthesized with different polymerization sequences leads to an understanding of the molecular bonding responsible for the tensile and rheological properties. From the interpretation of the wavenumber bands and associated molecular bonds, we find that the polymerization sequence most affects hydrogen bonding and aromatic ring bond energies. This work defines the relationships between chemistry, structure, processing, and properties of the IPN samples.

Deep Learning Based Prediction of Perovskite Lattice Parameters from Hirshfeld Surface Fingerprints.

This Letter describes the use of deep learning methods on Hirshfeld surface representations of crystal structure, as an automated means of predicting lattice parameters in cubic inorganic perovskites. While Hirshfeld Surface Analysis is a well-established tool in organic crystallography, we also introduce modified computational protocols for Hirshfeld Surface Analysis tailored specifically to account for nuanced but important differences dealing with inorganic crystals. We demonstrate how two-dimensional Hirshfeld surface fingerprints can serve as a rich "database" of information encoding the complexity of relationships between chemical bonding and bond geometry characteristics of perovskites. Our results are compared with other studies on lattice parameter prediction involving both experimental and computationally derived data, and it is shown that our approach is an improvement over other reported methods. The paper concludes by discussing how this work opens new avenues for data-driven high throughput computational predictions of structure-property relationships involving complex crystal chemistries.