Radial elasticity of multi-walled carbon nanotubes

We report an experimental and a theoretical study of the radial elasticity of multi-walled carbon nanotubes as a function of external radius. We use atomic force microscopy and apply small indentation amplitudes in order to stay in the linear elasticity regime. The number of layers for a given tube radius is inferred from transmission electron microscopy, revealing constant ratios of external to internal radii. This enables a comparison with molecular dynamics results, which also shed some light onto the applicability of Hertz theory in this context. Using this theory, we find a radial Young modulus strongly decreasing with increasing radius and reaching an asymptotic value of 30 +/- 10 GPa.Comment: 5 pages, 3 figure

3D characterization of CdSe nanoparticles attached to carbon nanotubes

The crystallographic structure of CdSe nanoparticles attached to carbon nanotubes has been elucidated by means of high resolution transmission electron microscopy and high angle annular dark field scanning transmission electron microscopy tomography. CdSe rod-like nanoparticles, grown in solution together with carbon nanotubes, undergo a morphological transformation and become attached to the carbon surface. Electron tomography reveals that the nanoparticles are hexagonal-based with the (001) planes epitaxially matched to the outer graphene layer.Comment: 7 pages, 8 figure

A disordered region retains the full protease inhibitor activity and the capacity to induce CD8+ T cells in vivo of the oral vaccine adjuvant U-Omp19

U-Omp19 is a bacterial protease inhibitor from Brucella abortus that inhibits gastrointestinal and lysosomal proteases, enhancing the half-life and immunogenicity of co-delivered antigens. U-Omp19 is a novel adjuvant that is in preclinical development with various vaccine candidates. However, the molecular mechanisms by which it exerts these functions and the structural elements responsible for these activities remain unknown. In this work, a structural, biochemical, and functional characterization of U-Omp19 is presented. Dynamic features of U-Omp19 in solution by NMR and the crystal structure of its C-terminal domain are described. The protein consists of a compact C-terminal beta-barrel domain and a flexible N-terminal domain. The latter domain behaves as an intrinsically disordered protein and retains the full protease inhibitor activity against pancreatic elastase, papain and pepsin. This domain also retains the capacity to induce CD8+ T cells in vivo of U-Omp19. This information may lead to future rationale vaccine designs using U-Omp19 as an adjuvant to deliver other proteins or peptides in oral formulations against infectious diseases, as well as to design strategies to incorporate modifications in its structure that may improve its adjuvanticity.Fil: Darriba, María Laura. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Pueblas Castro, Celeste. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Coria, Lorena M. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Bruno, Laura. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Cerutti, María Laura. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Chemes, Lucía B. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Cassataro, Juliana. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Pasquevich, Karina A. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Darriba, María Laura. Universidad Nacional de San Martín. Escuela de Bio y Nanotecnologías (EByN); Argentina.Fil: Pueblas Castro, Celeste. Universidad Nacional de San Martín. Escuela de Bio y Nanotecnologías (EByN); Argentina.Fil: Coria, Lorena M. Universidad Nacional de San Martín. Escuela de Bio y Nanotecnologías (EByN); Argentina.Fil: Bruno, Laura. Universidad Nacional de San Martín. Escuela de Bio y Nanotecnologías (EByN); Argentina.Fil: Cerutti, María Laura. Universidad Nacional de San Martín. Escuela de Bio y Nanotecnologías (EByN); Argentina.Fil: Chemes, Lucía B. Universidad Nacional de San Martín. Escuela de Bio y Nanotecnologías (EByN); Argentina.Fil: Cassataro, Juliana. Universidad Nacional de San Martín. Escuela de Bio y Nanotecnologías (EByN); Argentina.Fil: Pasquevich, Karina A. Universidad Nacional de San Martín. Escuela de Bio y Nanotecnologías (EByN); Argentina.Fil: Otero, Lisandro H. Fundación Instituto Leloir. Plataforma Argentina de Biología Estructural y Metabolómica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Klinke, Sebastián. Fundación Instituto Leloir. Plataforma Argentina de Biología Estructural y Metabolómica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Rasia, Rodolfo M. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario. Plataforma Argentina de Biología Estructural y Metabolómica; Argentina

An empirical Bayesian approach for model-based inference of cellular signaling networks

Background A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. Results As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF) signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. Conclusion In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements

Field-effect transistors assembled from functionalized carbon nanotubes

We have fabricated field effect transistors from carbon nanotubes using a novel selective placement scheme. We use carbon nanotubes that are covalently bound to molecules containing hydroxamic acid functionality. The functionalized nanotubes bind strongly to basic metal oxide surfaces, but not to silicon dioxide. Upon annealing, the functionalization is removed, restoring the electronic properties of the nanotubes. The devices we have fabricated show excellent electrical characteristics.Comment: 5 pages, 6 figure

Tungsten Oxide Nanorods Array and Nanobundle Prepared by Using Chemical Vapor Deposition Technique

Tungsten oxide (WO3) nanorods array prepared using chemical vapor deposition techniques was studied. The influence of oxygen gas concentration on the nanoscale tungsten oxide structure was observed; it was responsible for the stoichiometric and morphology variation from nanoscale particle to nanorods array. Experimental results also indicated that the deposition temperature was highly related to the morphology; the chemical structure, however, was stable. The evolution of the crystalline structure and surface morphology was analyzed by scanning electron microscopy, Raman spectra and X-ray diffraction approaches. The stoichiometric variation was indicated by energy dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy

Thermoelectric properties of lead chalcogenide core-shell nanostructures

We present the full thermoelectric characterization of nanostructured bulk PbTe and PbTe-PbSe samples fabricated from colloidal core-shell nanoparticles followed by spark plasma sintering. An unusually large thermopower is found in both materials, and the possibility of energy filtering as opposed to grain boundary scattering as an explanation is discussed. A decreased Debye temperature and an increased molar specific heat are in accordance with recent predictions for nanostructured materials. On the basis of these results we propose suitable core-shell material combinations for future thermoelectric materials of large electric conductivities in combination with an increased thermopower by energy filtering.Comment: 12 pages, 8 figure

CVD growth of carbon nanostructures from zirconia: mechanisms and a method for enhancing yield.

By excluding metals from synthesis, growth of carbon nanostructures via unreduced oxide nanoparticle catalysts offers wide technological potential. We report new observations of the mechanisms underlying chemical vapor deposition (CVD) growth of fibrous carbon nanostructures from zirconia nanoparticles. Transmission electron microscope (TEM) observation reveals distinct differences in morphological features of carbon nanotubes and nanofibers (CNTs and CNFs) grown from zirconia nanoparticle catalysts versus typical oxide-supported metal nanoparticle catalysts. Nanofibers borne from zirconia lack an observable graphitic cage consistently found with nanotube-bearing metal nanoparticle catalysts. We observe two distinct growth modalities for zirconia: (1) turbostratic CNTs 2-3 times smaller in diameter than the nanoparticle localized at a nanoparticle corner, and (2) nonhollow CNFs with approximately the same diameter as the nanoparticle. Unlike metal nanoparticle catalysts, zirconia-based growth should proceed via surface-bound kinetics, and we propose a growth model where initiation occurs at nanoparticle corners. Utilizing these mechanistic insights, we further demonstrate that preannealing of zirconia nanoparticles with a solid-state amorphous carbon substrate enhances growth yield.This material is based upon work supported by the National Science Foundation under Grant No. 1007793 and was also supported by Airbus group, Boeing, Embraer, Lockheed Martin, Saab AB, Hexcel, and TohoTenax through MIT’s Nano- Engineered Composite aerospace STructures (NECST) Consortium. This research was supported (in part) by the U.S. Army Research Office under Contract W911NF-13-D-0001. This work was performed in part at the Center for Nanoscale Systems (CNS), a member of the National Nanotechnology Infrastructure Network (NNIN), which is supported by the National Science Foundation under NSF Award No. ECS-0335765. CNS is part of Harvard University. This work was carried out in part through the use of MIT Microsystems Technology Laboratories. Stephan Hofmann acknowledges funding from EPSRC under grant EP/H047565/1. Piran Kidambi acknowledges the Lindemann Trust Fellowship.This is the final published version. It first appeared at http://pubs.acs.org/doi/abs/10.1021/ja509872y

A multiscale systems perspective on cancer, immunotherapy, and Interleukin-12

Monoclonal antibodies represent some of the most promising molecular targeted immunotherapies. However, understanding mechanisms by which tumors evade elimination by the immune system of the host presents a significant challenge for developing effective cancer immunotherapies. The interaction of cancer cells with the host is a complex process that is distributed across a variety of time and length scales. The time scales range from the dynamics of protein refolding (i.e., microseconds) to the dynamics of disease progression (i.e., years). The length scales span the farthest reaches of the human body (i.e., meters) down to the range of molecular interactions (i.e., nanometers). Limited ranges of time and length scales are used experimentally to observe and quantify changes in physiology due to cancer. Translating knowledge obtained from the limited scales observed experimentally to predict patient response is an essential prerequisite for the rational design of cancer immunotherapies that improve clinical outcomes. In studying multiscale systems, engineers use systems analysis and design to identify important components in a complex system and to test conceptual understanding of the integrated system behavior using simulation. The objective of this review is to summarize interactions between the tumor and cell-mediated immunity from a multiscale perspective. Interleukin-12 and its role in coordinating antibody-dependent cell-mediated cytotoxicity is used illustrate the different time and length scale that underpin cancer immunoediting. An underlying theme in this review is the potential role that simulation can play in translating knowledge across scales

Protein-based identification of quantitative trait loci associated with malignant transformation in two HER2+ cellular models of breast cancer

Background A contemporary view of the cancer genome reveals extensive rearrangement compared to normal cells. Yet how these genetic alterations translate into specific proteomic changes that underpin acquiring the hallmarks of cancer remains unresolved. The objectives of this study were to quantify alterations in protein expression in two HER2+ cellular models of breast cancer and to infer differentially regulated signaling pathways in these models associated with the hallmarks of cancer. Results A proteomic workflow was used to identify proteins in two HER2 positive tumorigenic cell lines (BT474 and SKBR3) that were differentially expressed relative to a normal human mammary epithelial cell line (184A1). A total of 64 (BT474-184A1) and 69 (SKBR3-184A1) proteins were uniquely identified that were differentially expressed by at least 1.5-fold. Pathway inference tools were used to interpret these proteins in terms of functionally enriched pathways in the tumor cell lines. We observed protein ubiquitination and apoptosis signaling pathways were both enriched in the two breast cancer models while IGF signaling and cell motility pathways were enriched in BT474 and amino acid metabolism were enriched in the SKBR3 cell line. Conclusion While protein ubiquitination and apoptosis signaling pathways were common to both the cell lines, the observed patterns of protein expression suggest that the evasion of apoptosis in each tumorigenic cell line occurs via different mechanisms. Evidently, apoptosis is regulated in BT474 via down regulation of Bid and in SKBR3 via up regulation of Calpain-11 as compared to 184A1