Lubrication at physiological pressures by polyzwitterionic brushes

The very low sliding friction at natural synovial joints, which have friction coefficients of mu < 0.002 at pressures up to 5 megapascals or more, has to date not been attained in any human-made joints or between model surfaces in aqueous environments. We found that surfaces in water bearing polyzwitterionic brushes that were polymerized directly from the surface can have m values as low as 0.0004 at pressures as high as 7.5 megapascals. This extreme lubrication is attributed primarily to the strong hydration of the phosphorylcholine-like monomers that make up the robustly attached brushes, and may have relevance to a wide range of human-made aqueous lubrication situations

Fluid and Diffusion Limits for Bike Sharing Systems

Bike sharing systems have rapidly developed around the world, and they are served as a promising strategy to improve urban traffic congestion and to decrease polluting gas emissions. So far performance analysis of bike sharing systems always exists many difficulties and challenges under some more general factors. In this paper, a more general large-scale bike sharing system is discussed by means of heavy traffic approximation of multiclass closed queueing networks with non-exponential factors. Based on this, the fluid scaled equations and the diffusion scaled equations are established by means of the numbers of bikes both at the stations and on the roads, respectively. Furthermore, the scaling processes for the numbers of bikes both at the stations and on the roads are proved to converge in distribution to a semimartingale reflecting Brownian motion (SRBM) in a $N^{2}$-dimensional box, and also the fluid and diffusion limit theorems are obtained. Furthermore, performance analysis of the bike sharing system is provided. Thus the results and methodology of this paper provide new highlight in the study of more general large-scale bike sharing systems.Comment: 34 pages, 1 figure

Potential for modulation of the hydrophobic effect inside chaperonins

Despite the spontaneity of some in vitro protein folding reactions, native folding in vivo often requires the participation of barrel-shaped multimeric complexes known as chaperonins. Although it has long been known that chaperonin substrates fold upon sequestration inside the chaperonin barrel, the precise mechanism by which confinement within this space facilitates folding remains unknown. In this study, we examine the possibility that the chaperonin mediates a favorable reorganization of the solvent for the folding reaction. We begin by discussing the effect of electrostatic charge on solvent-mediated hydrophobic forces in an aqueous environment. Based on these initial physical arguments, we construct a simple, phenomenological theory for the thermodynamics of density and hydrogen bond order fluctuations in liquid water. Within the framework of this model, we investigate the effect of confinement within a chaperonin-like cavity on the configurational free energy of water by calculating solvent free energies for cavities corresponding to the different conformational states in the ATP- driven catalytic cycle of the prokaryotic chaperonin GroEL. Our findings suggest that one function of chaperonins may be to trap unfolded proteins and subsequently expose them to a micro-environment in which the hydrophobic effect, a crucial thermodynamic driving force for folding, is enhanced

Experimental Tools to Study Molecular Recognition within the Nanoparticle Corona

Advancements in optical nanosensor development have enabled the design of sensors using synthetic molecular recognition elements through a recently developed method called Corona Phase Molecular Recognition (CoPhMoRe). The synthetic sensors resulting from these design principles are highly selective for specific analytes, and demonstrate remarkable stability for use under a variety of conditions. An essential element of nanosensor development hinges on the ability to understand the interface between nanoparticles and the associated corona phase surrounding the nanosensor, an environment outside of the range of traditional characterization tools, such as NMR. This review discusses the need for new strategies and instrumentation to study the nanoparticle corona, operating in both in vitro and in vivo environments. Approaches to instrumentation must have the capacity to concurrently monitor nanosensor operation and the molecular changes in the corona phase. A detailed overview of new tools for the understanding of CoPhMoRe mechanisms is provided for future applications.Juvenile Diabetes Research Foundation InternationalMcGovern Institute for Brain Research at MIT. Neurotechnology (MINT) ProgramNational Science Foundation (U.S.) (Postdoctoral Research Fellowship Award DBI-1306229)Burroughs Wellcome Fund (Grant Award 1013994)German Science Foundatio

A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model

A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP). In the proposed approach, vocabulary learning is performed using a hybrid generative-descriptive model. First, statistical relationships between parts are learned using a Minimum Conditional Entropy Clustering algorithm. Then, selection of descriptive parts is defined as a frequent subgraph discovery problem, and solved using a Minimum Description Length (MDL) principle. Finally, part compositions are constructed by compressing the internal data representation with discovered substructures. Shape representation and computational complexity properties of the proposed approach and algorithms are examined using six benchmark two-dimensional shape image datasets. Experiments show that CHOP can employ part shareability and indexing mechanisms for fast inference of part compositions using learned shape vocabularies. Additionally, CHOP provides better shape retrieval performance than the state-of-the-art shape retrieval methods.Comment: Paper : 17 pages. 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pp 566-581. Supplementary material can be downloaded from http://link.springer.com/content/esm/chp:10.1007/978-3-319-10578-9_37/file/MediaObjects/978-3-319-10578-9_37_MOESM1_ESM.pd

Quantitative Tissue Spectroscopy of Near Infrared Fluorescent Nanosensor Implants

Implantable, near infrared (nIR) fluorescent nanosensors are advantageous for in vivo monitoring of biological analytes since they can be rendered selective for particular target molecule while utilizing their unique optical properties and the nIR tissue transparency window for information transfer without an internal power source or telemetry. However, basic questions remain regarding the optimal encapsulation platform, geometrical properties, and concentration ranges required for effective signal to noise ratio through biological tissue. In this work, we systematically explore these variables quantitatively to optimize the performance of such optical nanosensors for biomedical applications. We investigate both alginate and polyethylene glycol (PEG) as model hydrogel systems, encapsulating d(GT)[subscript 15] ssDNA-wrapped single walled carbon nanotubes (SWNT) as model fluorescent nanoparticle sensors, responsive to riboflavin. Hydrogel sensors implanted 0.5 mm into thick tissue samples cause 50% reduction of initial fluorescence intensity, allowing an optical detection limit of 5.4 mm and 5.1 mm depth in tissue for alginate and PEG gels, respectively, at a SWNT concentration of 10 mg L−1, and 785 nm laser excitation of 80 mW and 30 s exposure. These findings are supported with in vivo nIR fluorescent imaging of SWNT hydrogels implanted subcutaneously in mice. For the case of SWNT, we find that the alginate system is preferable in terms of emission intensity, sensor response, rheological properties, and shelf life.National Institutes of Health (U.S.) (T32 Training Grant in Environmental Toxicology ES007020)National Cancer Institute (U.S.) (Grant P01 CA26731)National Institute of Environmental Health Sciences (Grant P30 ES002109)Arnold and Mabel Beckman Foundation (Young Investigator Award)National Science Foundation (U.S.) (Presidential Early Career Award for Scientists and Engineers)MIT-Technion FellowshipSamsung Scholarship FoundationSanofi Aventis (Firm) (Biomedical Innovation Grant