Immunization with a mucosal, post-fusion F/G protein-based polyanhydride nanovaccine protects neonatal calves against BRSV infection.

Human respiratory syncytial virus (HRSV) is a leading cause of death in young children and there are no FDA approved vaccines. Bovine RSV (BRSV) is antigenically similar to HRSV, and the neonatal calf model is useful for evaluation of HRSV vaccines. Here, we determined the efficacy of a polyanhydride-based nanovaccine encapsulating the BRSV post-fusion F and G glycoproteins and CpG, delivered prime-boost via heterologous (intranasal/subcutaneous) or homologous (intranasal/intranasal) immunization in the calf model. We compared the performance of the nanovaccine regimens to a modified-live BRSV vaccine, and to non-vaccinated calves. Calves receiving nanovaccine via either prime-boost regimen exhibited clinical and virological protection compared to non-vaccinated calves. The heterologous nanovaccine regimen induced both virus-specific cellular immunity and mucosal IgA, and induced similar clinical, virological and pathological protection as the commercial modified-live vaccine. Principal component analysis identified BRSV-specific humoral and cellular responses as important correlates of protection. The BRSV-F/G CpG nanovaccine is a promising candidate vaccine to reduce RSV disease burden in humans and animals.

Midshaft clavicle fractures: is anterior plating an acceptable alternative to superior plating?

PURPOSE: The purpose of this study was to determine whether anterior plating is better tolerated than superior plating for midshaft clavicle fractures. METHODS: This was a prospective non-randomized observational cohort study following operative vs. non-operative management of clavicle fractures from 2003 to 2018 at 7 level 1 academic trauma centers in the USA. The subset of patients treated with plate and screws is the basis for this comparative study. Adults aged 18-85 with closed clavicle fractures displaced over 100% or shortened by more than 1.5 cm were eligible for enrollment. Patients were followed for 2 years following enrollment. Allowable fixation methods at the discretion of the surgeon consisted of anterior-inferior or superior plating. A total of 412 patients were enrolled. Of these, 192 patients received either superior or anterior plating for a displaced clavicle fracture with complete documented prospective research forms capturing type of plating technique. The primary outcome measure was hardware removal (HWR). Secondary outcomes were Disability of the Arm Shoulder and Hand (DASH) score and Visual Analog Pain (VAP) score, and satisfaction score (1 = high satisfaction; 5 = low satisfaction). RESULTS: There was no difference in HWR rates (7.1% superior 9/127; 6.2% anterior 4/65, p = 0.81), VAP score (mean 1.5 SD 1.0 superior; mean 1.7 SD 0.6 anterior, p = 0.21), DASH score (mean 7.5 SD 12.4 superior; mean 5.2 SD 15.2 anterior; p = 0.18) or satisfaction score (mean 1.6 SD 1.0 superior; mean 1.7 SD 0.60 anterior, p = 0.18). CONCLUSION: There is no difference in HWR rates or functional outcomes when using a superior vs. anterior plating technique.

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.

Releasing chemical energy in spatiallyprogrammed ferroelectrics.

Chemical energy ferroelectrics are generally solid macromolecules showing spontaneous polarization and chemical bonding energy. These materials still suffer drawbacks, including the limited control of energy release rate, and thermal decomposition energy well below total chemical energy. To overcome these drawbacks, we report the integrated molecular ferroelectric and energetic material from machine learning-directed additive manufacturing coupled with the ice-templating assembly. The resultant aligned porous architecture shows a low density of 0.35 g cm-3, polarization-controlled energy release, and an anisotropic thermal conductivity ratio of 15. Thermal analysis suggests that the chlorine radicals react with macromolecules enabling a large exothermic enthalpy of reaction (6180 kJ kg-1). In addition, the estimated detonation velocity of molecular ferroelectrics can be tuned from 6.69 ± 0.21 to 7.79 ± 0.25 km s-1 by switching the polarization state. These results provide a pathway toward spatially programmed energetic ferroelectrics for controlled energy release rates.

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.

Proton switching molecular magnetoelectricity.

The convergence of proton conduction and multiferroics is generating a compelling opportunity to achieve strong magnetoelectric coupling and magneto-ionics, offering a versatile platform to realize molecular magnetoelectrics. Here we describe machine learning coupled with additive manufacturing to accelerate the design strategy for hydrogen-bonded multiferroic macromolecules accompanied by strong proton dependence of magnetic properties. The proton switching magnetoelectricity occurs in three-dimensional molecular heterogeneous solids. It consists of a molecular magnet network as proton reservoir to modulate ferroelectric polarization, while molecular ferroelectrics charging proton transfer to reversibly manipulate magnetism. The magnetoelectric coupling induces a reversible 29% magnetization control at ferroelectric phase transition with a broad thermal hysteresis width of 160 K (192 K to 352 K), while a room-temperature reversible magnetic modulation is realized at a low electric field stimulus of 1 kV cm-1. The findings of electrostatic proton transfer provide a pathway of proton mediated magnetization control in hierarchical molecular multiferroics.

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.

A Protective Vaccine against Johne's Disease in Cattle.

Johne's disease (JD) caused by Mycobacterium avium subsp. paratuberculosis (M. paratuberculosis) is a chronic infection characterized by the development of granulomatous enteritis in wild and domesticated ruminants. It is one of the most significant livestock diseases not only in the USA but also globally, accounting for USD 200-500 million losses annually for the USA alone with potential link to cases of Crohn's disease in humans. Developing safe and protective vaccines is of a paramount importance for JD control in dairy cows. The current study evaluated the safety, immunity and protective efficacy of a novel live attenuated vaccine (LAV) candidate with and without an adjuvant in comparison to an inactivated vaccine. Results indicated that the LAV, irrespective of the adjuvant presence, induced robust T cell immune responses indicated by proinflammatory cytokine production such as IFN-γ, IFN-α, TNF-α and IL-17 as well as strong response to intradermal skin test against M. paratuberculosis antigens. Furthermore, the LAV was safe with minimal tissue pathology. Finally, calves vaccinated with adjuvanted LAV did not shed M. paratuberculosis post-challenge, a much-desired characteristic of an effective vaccine against JD. Together, this data suggests a strong potential of testing LAV in field trials to curb JD in dairy herds.

Correlation of Multiplicity and Chemistry in Al x Ga1-xN Heterostructure via Atom Probe Tomography.

In this work, the correlation between composition and relative evaporation field was investigated by tracking the statistics of multi-hit detector events in atom probe tomography (APT). This approach is applied systematically to a GaN-based nitride heterostructure with five AlxGa1-xN layers of varying Al composition. The relative field evaporation and the percentage of multi-hit events were found to increase with higher Al concentration. Furthermore, the comparison of the relative evaporation fields of AlN with respect to the constituent ions is found to be less than GaN with respect to its constituent ions. Despite equivalent compositions between opposing interfaces of the same AlxGa1-xN interlayer, the rate of change in multiplicity exhibits a consistent asymmetric trend with a steeper slope across the AlxGa1-xN/GaN interface compared to the GaN/AlxGa1-xN interface. The AlxGa1-xN/GaN heterostructure serves as a test structure for exploring field evaporation and neighborhood chemistry, which can be applied to any material chemistry and particularly other nitride systems.

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.

Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams.

The use of machine learning techniques to expedite the discovery and development of new materials is an essential step towards the acceleration of a new generation of domain-specific highly functional material systems. In this paper, we use the test case of bulk metallic glasses to highlight the key issues in the field of high throughput predictions and propose a new probabilistic analysis of rules for glass forming ability using rough set theory. This approach has been applied to a broad range of binary alloy compositions in order to predict new metallic glass compositions. Our data driven approach takes into account not only a broad variety of thermodynamic, structural and kinetic based criteria, but also incorporates qualitative and descriptive attributes associated with eutectic points in phase diagrams. For the latter, we demonstrate the use of automated machine learning methods that go far beyond text recognition approaches by also being able to interpret phase diagrams. When combined with structural descriptors, this approach provides the foundations to develop a hierarchical probabilistic predication tool that can rank the feasibility of glass formation.

Functionalization promotes pathogen-mimicking characteristics of polyanhydride nanoparticle adjuvants.

Rational design of adjuvants and delivery systems will promote development of next-generation vaccines to control emerging and re-emerging diseases. To accomplish this, understanding the immune-enhancing properties of new adjuvants relative to those induced by natural infections can help with the development of pathogen-mimicking materials that will effectively initiate innate immune signaling cascades. In this work, the surfaces of polyanhydride nanoparticles composed of sebacic acid (SA) and 1,6-bis(p-carboxyphenoxy) hexane were decorated with an ethylene diamine spacer partially modified with either a glycolic acid linker or an α-1,2-linked di-mannopyranoside (di-mannose) to confer "pathogen-like" properties and enhance adjuvanticity. Co-incubation of linker-modified nanoparticles with dendritic cells (DCs) elicited significant increases in surface expression of MHC I, MHC II, CD86, and CD40, and enhanced secretion of IL-6, IL-12p40, and TNF-α. An 800% increase in uptake of ethylene-diamine-spaced, linker and di-mannose functionalized polyanhydride nanoparticles was also observed. Together, our data showed that linker-functionalized polyanhydride nanoparticles demonstrate similar patterns of uptake, intracellular trafficking, particle persistence, and innate activation as did DCs exposed to Yersinia pestis or Escherichia coli. These results set the stage for rational selection of adjuvant chemistries to induce pathogen-mimicking immune responses. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 2762-2771, 2017.

Combination Nanovaccine Demonstrates Synergistic Enhancement in Efficacy against Influenza.

H5N1 influenza virus has the potential to become a significant global health threat, and next generation vaccine technologies are needed. In this work, the combined efficacy of two nanoadjuvant platforms (polyanhydride nanoparticles and pentablock copolymer-based hydrogels) to induce protective immunity against H5N1 influenza virus was examined. Mice received two subcutaneous vaccinations (day 0 and 21) containing 10 µg of H5 hemagglutinin trimer alone or in combination with the nanovaccine platforms. Nanovaccine immunization induced high neutralizing antibody titers that were sustained through 70 days postimmunization. Finally, mice were intranasally challenged with A/H5N1 VNH5N1-PR8CDC-RG virus and monitored for 14 days. Animals receiving the combination nanovaccine had lower viral loads in the lung and weight loss after challenge in comparison to animals vaccinated with each platform alone. These data demonstrate the synergy between polyanhydride nanoparticles and pentablock copolymer-based hydrogels as adjuvants in the design of a more efficacious influenza vaccine.

Mapping Chemical Selection Pathways for Designing Multicomponent Alloys: an informatics framework for materials design.

A data driven methodology is developed for tracking the collective influence of the multiple attributes of alloying elements on both thermodynamic and mechanical properties of metal alloys. Cobalt-based superalloys are used as a template to demonstrate the approach. By mapping the high dimensional nature of the systematics of elemental data embedded in the periodic table into the form of a network graph, one can guide targeted first principles calculations that identify the influence of specific elements on phase stability, crystal structure and elastic properties. This provides a fundamentally new means to rapidly identify new stable alloy chemistries with enhanced high temperature properties. The resulting visualization scheme exhibits the grouping and proximity of elements based on their impact on the properties of intermetallic alloys. Unlike the periodic table however, the distance between neighboring elements uncovers relationships in a complex high dimensional information space that would not have been easily seen otherwise. The predictions of the methodology are found to be consistent with reported experimental and theoretical studies. The informatics based methodology presented in this study can be generalized to a framework for data analysis and knowledge discovery that can be applied to many material systems and recreated for different design objectives.

Extracting features buried within high density atom probe point cloud data through simplicial homology.

Feature extraction from Atom Probe Tomography (APT) data is usually performed by repeatedly delineating iso-concentration surfaces of a chemical component of the sample material at different values of concentration threshold, until the user visually determines a satisfactory result in line with prior knowledge. However, this approach allows for important features, buried within the sample, to be visually obscured by the high density and volume (~10(7) atoms) of APT data. This work provides a data driven methodology to objectively determine the appropriate concentration threshold for classifying different phases, such as precipitates, by mapping the topology of the APT data set using a concept from algebraic topology termed persistent simplicial homology. A case study of Sc precipitates in an Al-Mg-Sc alloy is presented demonstrating the power of this technique to capture features, such as precise demarcation of Sc clusters and Al segregation at the cluster boundaries, not easily available by routine visual adjustment.

Automated voxelization of 3D atom probe data through kernel density estimation.

Identifying nanoscale chemical features from atom probe tomography (APT) data routinely involves adjustment of voxel size as an input parameter, through visual supervision, making the final outcome user dependent, reliant on heuristic knowledge and potentially prone to error. This work utilizes Kernel density estimators to select an optimal voxel size in an unsupervised manner to perform feature selection, in particular targeting resolution of interfacial features and chemistries. The capability of this approach is demonstrated through analysis of the γ / γ' interface in a Ni-Al-Cr superalloy.

Hemagglutinin-based polyanhydride nanovaccines against H5N1 influenza elicit protective virus neutralizing titers and cell-mediated immunity.

H5N1 avian influenza is a significant global concern with the potential to become the next pandemic threat. Recombinant subunit vaccines are an attractive alternative for pandemic vaccines compared to traditional vaccine technologies. In particular, polyanhydride nanoparticles encapsulating subunit proteins have been shown to enhance humoral and cell-mediated immunity and provide protection upon lethal challenge. In this work, a recombinant H5 hemagglutinin trimer (H53) was produced and encapsulated into polyanhydride nanoparticles. The studies performed indicated that the recombinant H53 antigen was a robust immunogen. Immunizing mice with H53 encapsulated into polyanhydride nanoparticles induced high neutralizing antibody titers and enhanced CD4(+) T cell recall responses in mice. Finally, the H53-based polyanhydride nanovaccine induced protective immunity against a low-pathogenic H5N1 viral challenge. Informatics analyses indicated that mice receiving the nanovaccine formulations and subsequently challenged with virus were similar to naïve mice that were not challenged. The current studies provide a basis to further exploit the advantages of polyanhydride nanovaccines in pandemic scenarios.

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.

A systems approach to designing next generation vaccines: combining α-galactose modified antigens with nanoparticle platforms.

Innovative vaccine platforms are needed to develop effective countermeasures against emerging and re-emerging diseases. These platforms should direct antigen internalization by antigen presenting cells and promote immunogenic responses. This work describes an innovative systems approach combining two novel platforms, αGalactose (αGal)-modification of antigens and amphiphilic polyanhydride nanoparticles as vaccine delivery vehicles, to rationally design vaccine formulations. Regimens comprising soluble αGal-modified antigen and nanoparticle-encapsulated unmodified antigen induced a high titer, high avidity antibody response with broader epitope recognition of antigenic peptides than other regimen. Proliferation of antigen-specific CD4(+) T cells was also enhanced compared to a traditional adjuvant. Combining the technology platforms and augmenting immune response studies with peptide arrays and informatics analysis provides a new paradigm for rational, systems-based design of next generation vaccine platforms against emerging and re-emerging pathogens.