Multicenter, Prospective, Observational Study of Nonoperative Versus Operative Treatment for High-Energy Midshaft Clavicle Fractures.

Objectives: Evaluate if nonoperative or operative treatment of displaced clavicle fractures delivers reduced rates of nonunion and improved Disability of the Arm, Shoulder, and Hand (DASH) scores.

Design: Multicenter, prospective, observational.

Setting: Seven Level 1 Trauma Centers in the United States.

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 heterologous (intranasal/subcutaneous) or homologous (intranasal/intranasal) immunization in the calf model.

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.

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.

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.

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 and Asian tiger shrimp . 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.

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.

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.

A Protective Vaccine against Johne's Disease in Cattle.

Johne's disease (JD) caused by subsp. () 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.

Correlation of Multiplicity and Chemistry in Al GaN 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

An introduction to orthopaedic coding and billing.

Coding is the process of identifying descriptive terms and codes for diagnoses and medical services, which result from patient interactions with physicians and other healthcare providers. This information is organized in alpha and/or numeric fashion and may be used for charge submission, performance measurement, and data collection for emerging technology, services, and procedures. Under the Health Insurance Portability and Accountability Act, The Department of Health and Human Services designated the International classification of Diseases and Current Procedural Terminology as the national standard code sets for healthcare professional services and procedures.

Use of Continuous Passive Motion in the Postoperative Treatment of Intra-Articular Knee Fractures.

Background: The use of continuous passive motion in the postoperative treatment of intra-articular fractures around the knee is increasing. The purpose of this study was to determine the effects of a continuous passive motion device on knee range of motion after operative treatment of intra-articular fractures around the knee.

Methods: Forty patients with intra-articular fractures of either the proximal part of the tibia or the distal end of the femur were prospectively randomized to the use of continuous passive motion or standardized physical therapy in the immediate postoperative period for forty-eight hours.

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.

Mapping energetics of atom probe evaporation events through first principles calculations.

The purpose of this work is to use atomistic modeling to determine accurate inputs into the atom probe tomography (APT) reconstruction process. One of these inputs is evaporation field; however, a challenge occurs because single ions and dimers have different evaporation fields. We have calculated the evaporation field of Al and Sc ions and Al-Al and Al-Sc dimers from an L1₂-Al₃Sc surface using ab initio calculations and with a high electric field applied to the surface.

Data mining for isotope discrimination in atom probe tomography.

Ions with similar time-of-flights (TOF) can be discriminated by mapping their kinetic energy. While current generation position-sensitive detectors have been considered insufficient for capturing the isotope kinetic energy, we demonstrate in this paper that statistical learning methodologies can be used to capture the kinetic energy from all of the parameters currently measured by mathematically transforming the signal. This approach works because the kinetic energy is sufficiently described by the descriptors on the potential, the material, and the evaporation process within atom probe tomography (APT).