Hasil untuk "Engineering"

Yujie Liang, Li Duan, Jianping Lu et al.

Exosomes are cell-derived nanovesicles that are involved in the intercellular transportation of materials. Therapeutics, such as small molecules or nucleic acid drugs, can be incorporated into exosomes and then delivered to specific types of cells or tissues to realize targeted drug delivery. Targeted delivery increases the local concentration of therapeutics and minimizes side effects. Here, we present a detailed review of exosomes engineering through genetic and chemical methods for targeted drug delivery. Although still in its infancy, exosome-mediated drug delivery boasts low toxicity, low immunogenicity, and high engineerability, and holds promise for cell-free therapies for a wide range of diseases.

Carlos F. Guimarães, L. Gasperini, A. Marques et al.

Gerry L. Koons, M. Diba, A. Mikos

Tal Dvir, Brian P. Timko, D. Kohane et al.

Tissue engineering aims at developing functional substitutes for damaged tissues and organs. Before transplantation, cells are generally seeded on biomaterial scaffolds that recapitulate the extracellular matrix and provide cells with information that is important for tissue development. Here we review the nanocomposite nature of the extracellular matrix, describe the design considerations for different tissues and discuss the impact of nanostructures on the properties of scaffolds and their uses in monitoring the behaviour of engineered tissues. We also examine the different nanodevices used to trigger certain processes for tissue development, and offer our view on the principal challenges and prospects of applying nanotechnology in tissue engineering.

Saleema Amershi, Andrew Begel, C. Bird et al.

Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services. This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges. In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components - models may be "entangled" in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable to other organizations.

L. Giray

A. Luque, S. Hegedus

R. Ross

I. Towhata

M. Himmel, S. Ding, David K. Johnson et al.

Melinda Y. Han, B. Ozyilmaz, Yuanbo Zhang et al.

We investigate electronic transport in lithographically patterned graphene ribbon structures where the lateral confinement of charge carriers creates an energy gap near the charge neutrality point. Individual graphene layers are contacted with metal electrodes and patterned into ribbons of varying widths and different crystallographic orientations. The temperature dependent conductance measurements show larger energy gaps opening for narrower ribbons. The sizes of these energy gaps are investigated by measuring the conductance in the nonlinear response regime at low temperatures. We find that the energy gap scales inversely with the ribbon width, thus demonstrating the ability to engineer the band gap of graphene nanostructures by lithographic processes.

Jeanie L. Drury, D. Mooney

W. Koechner

R. Studer, Richard Benjamins, D. Fensel

M. P. Lutolf, M. P. Lutolf, J. Hubbell

D. Schmidt

A. Gupta, Mona Gupta

M. Gen, Runwei Cheng

S. Hollister

F. Brooks