Successful employment of 3D printing for delivery of therapeutic biomolecules requires protection of their bioactivity on exposure to potentially inactivating conditions. Although intermediary encapsulation of the biomolecules in polymeric particulate delivery vehicles is a promising strategy for this objective, the inclusion of such particles in 3D printing formulations may critically impact the accuracy or precision of 3D printed scaffolds relative to their intended designed architectures, as well as the degradation behavior of both the scaffolds and the included particles. The present work aimed to elucidate the effect of poly(d,l-lactic-co-glycolic acid) particle size and loading concentration on material accuracy, machine precision, and degradation of 3D printed poly(ɛ-caprolactone)-based scaffolds. Using a main effects analysis, the sizes and loading concentrations of particle delivery vehicles investigated were found to have neither a beneficial nor disadvantageous influence on the metrics of printing quality such as material accuracy and machine precision. Meanwhile, particle loading concentration was determined to influence degradation rate, whereas printing temperature affected the trends in composite weight-average molecular weight. Neither of the two particle-related parameters (concentration nor diameter) was found to exhibit a significant effect on intra-fiber nor inter-fiber porosity. These findings evidence the capacity for controlled loading of particulate delivery vehicles in 3D printed scaffolds while preserving construct accuracy and precision, and with predictable dictation of composite degradation behavior for potential controlled release of encapsulated biomolecules.

Over the past few years, we have seen an increased need to analyze the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method to separate and capture the signal and noise components of the series. Traditionally, wavelet thresholding methods rely on the discrete wavelet transform (DWT), which is a static thresholding technique that may not capture the time series of the estimated variance in the additive noise process. We introduce a novel continuous wavelet transform (CWT) dynamically optimized multivariate thresholding method (WaveL2E). Applying this method, we are simultaneously able to separate and capture the signal and noise components while estimating the dynamic noise variance. Our method shows improved results when compared to well-known methods, especially for high-frequency signal-rich time series, typically observed in finance.

Osteosarcoma (OS) is a genetically diverse bone cancer that lacks a consistent targetable mutation. Recent studies suggest the IGF/PI3K/mTOR pathway and YAP/TAZ paralogs regulate cell fate and proliferation in response to biomechanical cues within the tumor microenvironment. How this occurs and their implication upon osteosarcoma survival, remains poorly understood. Here, we show that IGF-1R can translocate into the nucleus, where it may act as part of a transcription factor complex. To explore the relationship between YAP/TAZ and total and nuclear phosphorylated IGF-1R (pIGF-1R), we evaluated sequential tumor sections from a 37-patient tissue microarray by confocal microscopy. Next, we examined the relationship between stained markers, clinical disease characteristics, and patient outcomes. The nuclear to cytoplasmic ratios (N:C ratio) of YAP and TAZ strongly correlated with nuclear pIGF-1R (r = 0.522, p = 0.001 for each pair). Kaplan–Meier analyses indicated that nuclear pIGF-1R predicted poor overall survival, a finding confirmed in the Cox proportional hazards model. Though additional investigation in a larger prospective study will be required to validate the prognostic accuracy of these markers, our results may have broad implications for the new class of YAP, TAZ, AXL, or TEAD inhibitors that have reached early phase clinical trials this year.

As modern data analysis pushes the boundaries of classical statistics, it is timely to reexamine alternate approaches to dealing with outliers in multiple regression. As sample sizes and the number of predictors increase, interactive methodology becomes less effective. Likewise, with limited understanding of the underlying contamination process, diagnostics are likely to fail as well. In this article, we advocate for a non-likelihood procedure that attempts to quantify the fraction of bad data as a part of the estimation step. These ideas also allow for the selection of important predictors under some assumptions. As there are many robust algorithms available, running several and looking for interesting differences is a sensible strategy for understanding the nature of the outliers.

Growth factors such as bone morphogenetic protein-2 (BMP-2) are potent tools for tissue engineering. Three-dimensional (3D) printing offers a potential strategy for delivery of BMP-2 from polymeric constructs; however, these biomolecules are sensitive to inactivation by the elevated temperatures commonly employed during extrusion-based 3D printing. Therefore, we aimed to correlate printing temperature to the bioactivity of BMP-2 released from 3D printed constructs composed of a model polymer, poly(propylene fumarate). Following encapsulation of BMP-2 in poly(DL-lactic-co-glycolic acid) particles, growth factor-loaded fibers were fabricated at three different printing temperatures. Resulting constructs underwent 28 days of aqueous degradation for collection of released BMP-2. Supernatants were then assayed for the presence of bioactive BMP-2 using a cellular assay for alkaline phosphatase activity. Cumulative release profiles indicated that BMP-2 released from constructs that were 3D printed at physiologic and intermediate temperatures exhibited comparable total amounts of bioactive BMP-2 release as those encapsulated in non-printed particulate delivery vehicles. Meanwhile, the elevated printing temperature of 90 °C resulted in a decreased amount of total bioactive BMP-2 release from the fibers. These findings elucidate the effects of elevated printing temperatures on BMP-2 bioactivity during extrusion-based 3D printing, and enlighten polymeric material selection for 3D printing with growth factors.

Here, we demonstrate the in vivo efficacy of glucose microparticles (GMPs) to serve as porogens within calcium phosphate cements (CPCs) to obtain a fast-degrading bone substitute material. Composites were fabricated incorporating 20 wt% GMPs at two different GMP size ranges (100-150 μm (GMP-S) and 150-300 μm (GMP-L)), while CPC containing 20 wt% poly(lactic-co-glycolic acid) microparticles (PLGA) and plain CPC served as controls. After 2 and 8 weeks implantation in a rat femoral condyle defect model, specimens were retrieved and analyzed for material degradation and bone formation. Histologically, no adverse tissue response to any of the CPC-formulations was observed. All CPC-porogen formulations showed faster degradation compared to plain CPC control, but only GMP-containing formulations showed higher amounts of new bone formation compared to plain CPC controls. After 8 weeks, only CPC-porogen formulations with GMP-S or PLGA porogens showed higher degradation compared to plain CPC controls. Overall, the inclusion of GMPs into CPCs resulted in a macroporous structure that initially accelerated the generation of new bone. These findings highlight the efficacy of a novel approach that leverages simple porogen properties to generate porous CPCs with distinct degradation and bone regeneration profiles.

This work investigated the effect of poly(l-lysine) (PLL) molecular weight and concentration on chondrogenesis of cocultures of mesenchymal stem cells (MSCs) and articular chondrocytes (ACs) in PLL-loaded hydrogels. An injectable dual-network hydrogel composed of a poly(N-isopropylacrylamide)-based synthetic thermogelling macromer and a chondroitin sulfate-based biological network was leveraged as a model to deliver PLL and encapsulate the two cell populations. Incorporation of PLL into the hydrogel did not affect the hydrogel's swelling properties and degradation characteristics, nor the viability of encapsulated cells. Coculture groups demonstrated higher type II collagen expression compared to the MSC monoculture group. Expression of hypertrophic phenotype was also limited in the coculture groups. Histological analysis indicated that the ratio of MSCs to ACs was an accurate predictor of the degree of long-term chondrogenesis, while the presence of PLL was shown to have a more substantial short-term effect. Altogether, this study demonstrates that coculturing MSCs with ACs can greatly enhance the chondrogenicity of the overall cell population and offers a platform to further elucidate the short- and long-term effect of polycationic factors on the chondrogenesis of MSC and AC cocultures.

In this study of three-dimensional (3D) printed composite beta-tricalcium phosphate (beta-TCP)-/hydroxyapatite/poly(e-caprolactone)-based constructs, the effects of vertical compositional ceramic gradients and architectural porosity gradients on the osteogenic differentiation of rabbit bone marrow-derived mesenchymal stem cells (MSCs) were investigated. Specifically, three different concentrations of beta-TCP (0, 10, and 20 wt%) and three different porosities (33% +/- 4%, 50% +/- 4%, and 65% +/- 3%) were examined to elucidate the contributions of chemical and physical gradients on the biochemical behavior of MSCs and the mineralized matrix production within a 3D culture system. By delaminating the constructs at the gradient transition point, the spatial separation of cellular phenotypes could be specifically evaluated for each construct section. Results indicated that increased concentrations of beta-TCP resulted in upregulation of osteogenic markers, including alkaline phosphatase activity and mineralized matrix development. Furthermore, MSCs located within regions of higher porosity displayed a more mature osteogenic phenotype compared to MSCs in lower porosity regions. These results demonstrate that 3D printing can be leveraged to create multiphasic gradient constructs to precisely direct the development and function of MSCs, leading to a phenotypic gradient. Impact Statement In this study, three-dimensional (3D) printed ceramic/polymeric constructs containing discrete vertical gradients of both composition and porosity were fabricated to precisely control the osteogenic differentiation of mesenchymal stem cells. By making simple alterations in construct architecture and composition, constructs containing heterogenous populations of cells were generated, where gradients in scaffold design led to corresponding gradients in cellular phenotype. The study demonstrates that 3D printed multiphasic composite constructs can be leveraged to create complex heterogeneous tissues and interfaces.

In this study of 3D printed composite β-tricalcium phosphate (β-TCP)/hydroxyapatite (HA)/poly(ε-caprolactone) (PCL)-based constructs, the effects of vertical compositional ceramic gradients and architectural porosity gradients on the osteogenic differentiation of rabbit bone marrow derived mesenchymal stem cells (MSCs) were investigated. Specifically, three different concentrations of β-TCP (0, 10, and 20 wt%) and three different porosities (33 ± 4 %, 50 ± 4 %, and 65 ± 3 %) were examined to elucidate the contributions of chemical and physical gradients on the biochemical behavior of MSCs and the mineralized matrix production within a 3D culture system. By delaminating the constructs at the gradient transition point, the spatial separation of cellular phenotypes could be specifically evaluated for each construct section. Results indicated that increased concentrations of β-TCP resulted in up-regulation of osteogenic markers including alkaline phosphatase activity and mineralized matrix development. Furthermore, MSCs located within regions of higher porosity displayed a more mature osteogenic phenotype compared to MSCs in lower porosity regions. These results demonstrate that 3D printing can be leveraged to create multiphasic gradient constructs to precisely direct the development and function of MSCs, leading to a phenotypic gradient.

Density Estimation is one of the central areas of statistics whose purpose is to estimate the probability density function underlying the observed data. It serves as a building block for many tasks in statistical inference, visualization, and machine learning. Density Estimation is widely adopted in the domain of unsupervised learning especially for the application of clustering. As big data become pervasive in almost every area of data sciences, analyzing high-dimensional data that have many features and variables appears to be a major focus in both academia and industry. High-dimensional data pose challenges not only from the theoretical aspects of statistical inference, but also from the algorithmic/computational considerations of machine learning and data analytics. This paper reviews a collection of selected nonparametric density estimation algorithms for high-dimensional data, some of them are recently published and provide interesting mathematical insights. The important application domain of nonparametric density estimation, such as { modal clustering}, are also included in this paper. Several research directions related to density estimation and high-dimensional data analysis are suggested by the authors.

Current in vitro methods for assessing cancer biology and therapeutic response rely heavily on monolayer cell culture on hard, plastic surfaces that do not recapitulate essential elements of the tumor microenvironment. While a host of tumor models exist, most are not engineered to control the physical properties of the microenvironment and thus may not reflect the effects of mechanotransduction on tumor biology. Utilizing coaxial electrospinning, we developed three-dimensional (3D) tumor models with tunable mechanical properties in order to elucidate the effects of substrate stiffness and tissue architecture in osteosarcoma. Mechanical properties of coaxial electrospun meshes were characterized with a series of macroscale testing with uniaxial tensile testing and microscale testing utilizing atomic force microscopy on single fibers. Calculated moduli in our models ranged over three orders of magnitude in both macroscale and microscale testing. Osteosarcoma cells responded to decreasing substrate stiffness in 3D environments by increasing nuclear localization of Hippo pathway effectors, YAP and TAZ, while downregulating total YAP. Additionally, a downregulation of the IGF-1R/mTOR axis, the target of recent clinical trials in sarcoma, was observed in 3D models and heralded increased resistance to combination chemotherapy and IGF-1R/mTOR targeted agents compared to monolayer controls. In this study, we highlight the necessity of incorporating mechanical cues in cancer biology investigation and the complexity in mechanotransduction as a confluence of stiffness and culture architecture. Our models provide a versatile, mechanically variable substrate on which to study the effects of physical cues on the pathogenesis of tumors. Statement of significance The tumor microenvironment plays a critical role in cancer pathogenesis. In this work, we engineered 3D, mechanically tunable, coaxial electrospun environments to determine the roles of the mechanical environment on osteosarcoma cell phenotype, morphology, and therapeutic response. We characterize the effects of varying macroscale and microscale stiffnesses in 3D environments on the localization and expression of the mechanoresponsive proteins, YAP and TAZ, and evaluate IGF-1R/mTOR pathway activation, a target of recent clinical trials in sarcoma. Increased nuclear YAP/TAZ was observed as stiffness in 3D was decreased. Downregulation of the IGF-1R/mTOR cascade in all 3D environments was observed. Our study highlights the complexity of mechanotransduction in 3D culture and represents a step towards controlling microenvironmental elements in in vitro cancer investigations. (C) 2019 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Recent developments in 3D printing (3DP) research have led to a variety of scaffold designs and techniques for osteochondral tissue engineering; however, the simultaneous incorporation of multiple types of gradients within the same construct remains a challenge. Herein, we describe the fabrication and mechanical characterization of porous poly(ε-caprolactone) (PCL) and PCL-hydroxyapatite (HA) scaffolds with incorporated vertical porosity and ceramic content gradients via a multimaterial extrusion 3DP system. Scaffolds of 0 wt% HA (PCL), 15 wt% HA (HA15), or 30 wt% HA (HA30) were fabricated with uniform composition and porosity (using 0.2 mm, 0.5 mm, or 0.9 mm on-center fiber spacing), uniform composition and gradient porosity, and gradient composition (PCL-HA15-HA30) and porosity. Micro-CT imaging and porosity analysis demonstrated the ability to incorporate both vertical porosity and pore size gradients and a ceramic gradient, which collectively recapitulate gradients found in native osteochondral tissues. Uniaxial compression testing demonstrated an inverse relationship between porosity, ϕ, and compressive modulus, E, and yield stress, σy, for uniform porosity scaffolds, however, no differences were observed as a result of ceramic incorporation. All scaffolds demonstrated compressive moduli within the appropriate range for trabecular bone, with average moduli between 86 ± 14-220 ± 26 MPa. Uniform porosity and pore size scaffolds for all ceramic levels had compressive moduli between 205 ± 37-220 ± 26 MPa, 112 ± 13-118 ± 23 MPa, and 86 ± 14-97 ± 8 MPa respectively for porosities ranging between 14 ± 4-20 ± 6%, 36 ± 3-43 ± 4%, and 54 ± 2-57 ± 2%, with the moduli and yield stresses of low porosity scaffolds being significantly greater (p < 0.05) than those of all other groups. Single (porosity) gradient and dual (composition/porosity) gradient scaffolds demonstrated compressive properties similar (p > 0.05) to those of the highest porosity uniform scaffolds (porosity gradient scaffolds 98 ± 23-107 ± 6 MPa, and 102 ± 7 MPa for dual composition/porosity gradient scaffolds), indicating that these properties are more heavily influenced by the weakest section of the gradient. The compression data for uniform scaffolds were also readily modeled, yielding scaling laws of the form E ∼ (1 - ϕ)1.27 and σy ∼ (1 - ϕ)1.37, which demonstrated that the compressive properties evaluated in this study were well-aligned with expectations from previous literature and were readily modeled with good fidelity independent of polymer scaffold geometry and ceramic content. All uniform scaffolds were similarly deformed and recovered despite different porosities, while the large-pore sections of porosity gradient scaffolds were significantly more deformed than all other groups, indicating that porosity may not be an independent factor in determining strain recovery. Moving forward, the technique described here will serve as the template for more complex multimaterial constructs with bioactive cues that better match native tissue physiology and promote tissue regeneration. STATEMENT OF SIGNIFICANCE: This manuscript describes the fabrication and mechanical characterization of "dual" porosity/ceramic content gradient scaffolds produced via a multimaterial extrusion 3D printing system for osteochondral tissue engineering. Such scaffolds are designed to better address the simultaneous gradients in architecture and mineralization found in native osteochondral tissue. The results of this study demonstrate that this technique may serve as a template for future advances in 3D printing technology that may better address the inherent complexity in such heterogeneous tissues.

University ranking is a popular yet controversial endeavor. Most rankings are based on both public data, such as student test scores and retention rates, and proprietary data, such as school reputation as perceived by high school counselors and academic peers. The weights applied to these characteristics to compute the rankings are often determined in a subjective fashion. Of significant importance in the academic field, the Carnegie Classification was developed by the Carnegie Foundation for the Advancement of Teaching. It has been updated approximately every 5 years since 1973, most recently in February 2016. Based on bivariate scores, Carnegie assigns one of three classes (R1/R2/R3) to doctorate-granting universities according to their level of research activity. The Carnegie methodology uses only publicly available data and determines weights via principal component analysis. In this article, we review Carnegie’s stated goals and the extent to which their methodology achieves those goals. In particular, we examine Carnegie’s separation of aggregate and per capita (per tenured/tenure-track faculty member) variables and its use of two separate principal component analyses on each; the resulting bivariate scores are very highly correlated. We propose and evaluate two alternatives and provide a graphical tool for evaluating and comparing the three scenarios.

Regularization is an important component of predictive model building. The hybrid bootstrap is a regularization technique that functions similarly to dropout except that features are resampled from other training points rather than replaced with zeros. We show that the hybrid bootstrap offers superior performance to dropout. We also present a sampling based technique to simplify hyperparameter choice. Next, we provide an alternative sampling technique for convolutional neural networks. Finally, we demonstrate the efficacy of the hybrid bootstrap on non-image tasks using tree-based models.

The Good Judgment Team led by psychologists P.Tetlock and B.Mellers of the University of Pennsylvania was the most successful of five research projects sponsored through 2015 by the Intelligence Advanced Research Projects Activity to develop improved group forecast aggregation algorithms. Each team had at least 10 algorithms under continuous development and evaluation over the 4-year project. The mean Brier score was used to rank the algorithms on approximately 130 questions concerning categorical geopolitical events each year. An algorithm would return aggregate probabilities for each question based on the probabilities provided per question by thousands of individuals, who had been recruited by the Good Judgment Team. This paper summarizes the theorized basis and implementation of one of the two most accurate algorithms at the conclusion of the Good Judgment Project. The algorithm incorporated a number of pre- and postprocessing steps, and relied upon a minimum distance robust regression method called L2E. The algorithm was just edged out by a variation of logistic regression, which has been described elsewhere. Work since the official conclusion of the project has led to an even smaller gap.

Abstract The “density estimation” article highlights the dual roles of the graphical exploration of data as well as the nonparametric estimation of probabilities. Estimators described in detail include the histogram, frequency polygon, averaged shifted histogram, and kernel method. The basic theory of estimation is outlined. Data‐based calibration by cross‐validation is described. Several concrete examples are given.

In this work, we describe the synthesis and characterization of variants of poly(diol fumarate) and poly(diol fumarate-co-succinate). Through a Fischer esterification, α,ω-diols and dicarboxylic acids were polymerized to form aliphatic polyester comacromers. Because of the carbon-carbon double bond of fumaric acid, incorporating it into the macromer backbone structure resulted in unsaturated chains. By choosing α,ω-diols of different lengths (1,6-hexanediol, 1,8-octanediol, and 1,10-decanediol) and controlling the amount of fumaric acid in the dicarboxylic acid monomer feed (33, 50, and 100 mol %), nine diol-based macromer variants were synthesized and characterized for molecular weight, number of unsaturated bonds per chain, and thermal properties. Degradation and in vitro cytotoxicity were also measured in a subset of macromers. As proof-of-principle, macromer networks were photo-cross-linked to demonstrate the ability to perform free radical addition using the unsaturated macromer backbone. Cross-linked macromer networks were also characterized for physicochemical properties (swelling, sol fraction, compressive modulus) based on diol length and amount of unsaturated bonds. A statistical model was built using data generated from these diol-based macromers and macromer networks to evaluate the impact of monomer inputs on final macromer and macromer network properties. With the ability to be modified by free radical addition, biodegradable unsaturated polyesters serve as important macromers in the design of devices such as drug delivery vehicles and tissue scaffolds. Given the ability to extensively control final macromer properties based on monomer input parameters, poly(diol fumarate) and poly(diol fumarate-co-succinate) represent an exciting new class of macromers.

3D printing has emerged as an important technique for fabricating tissue engineered scaffolds. However, systematic evaluations of biomaterials for 3D printing have not been widely investigated. We evaluated poly(propylene fumarate) (PPF) as a model material for extrusion-based printing applications. A full-factorial design evaluating the effects of four factors (PPF concentration, printing pressure, printing speed, and programmed fiber spacing) on viscosity, fiber diameter, and pore size was performed layer-by-layer on 3D scaffolds. We developed a linear model of printing solution viscosity, where concentration of PPF had the greatest effect on viscosity, and the polymer exhibited shear thinning behavior. Additionally, linear models of pore size and fiber diameter revealed that fiber spacing and pressure had the greatest effect on pore size and fiber diameter, respectively, but interplay among the factors also influenced scaffold architecture. This study serves as a platform to determine if novel biomaterials are suitable for extrusion-based 3D printing applications.

Antibiotic-releasing porous poly(methyl methacrylate) (PMMA) space maintainers, comprising PMMA with an aqueous porogen and a poly(DL-lactic-co-glycolic acid) (PLGA) antibiotic carrier, have been developed to facilitate local delivery of antibiotics and tissue integration. In this study, clindamycin-loaded space maintainers were used to investigate the effects of antibiotic release kinetics and dose upon bacterial clearance and bone and soft tissue healing in a pathogen-contaminated rabbit mandibular defect. Three formulations were fabricated for either high dose burst release (7 days) or with PLGA microparticles for extended release (28 days) at high and low dose. Although inoculated bacteria were not recovered from any specimens, the burst release formulation showed less inflammation and fibrous capsule formation and more bone formation close to the implant than the low dose extended release formulation by histologic analysis. These results suggest that local antibiotic release kinetics and dose affect soft and hard tissue healing independent from its ability to clear bacteria.