4 results on '"Josh K Smith"'
Search Results
2. Trimethylamine N-oxide–derived zwitterionic polymers: A new class of ultralow fouling bioinspired materials
- Author
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Shaoyi Jiang, Zhefan Yuan, Bowen Li, Jim Pfaendtner, Josh K. Smith, Yuwei He, Kan Wu, Jinrong Ma, Priyesh Jain, Hsiang-Chieh Hung, and Xiaojie Lin
- Subjects
Biofouling ,Polymers ,Materials Science ,Nanotechnology ,Trimethylamine N-oxide ,Biocompatible Materials ,02 engineering and technology ,Molecular Dynamics Simulation ,010402 general chemistry ,01 natural sciences ,chemistry.chemical_compound ,Molecular dynamics ,Methylamines ,Mice ,Adsorption ,Superhydrophilicity ,Cell Adhesion ,Animals ,Humans ,Research Articles ,Serum Albumin ,chemistry.chemical_classification ,Multidisciplinary ,Fouling ,Chemistry ,SciAdv r-articles ,Polymer ,Surface Plasmon Resonance ,021001 nanoscience & nanotechnology ,0104 chemical sciences ,Mice, Inbred C57BL ,NIH 3T3 Cells ,0210 nano-technology ,Ethylene glycol ,Protein adsorption ,Research Article - Abstract
Superhydrophilic zwitterionic polymers derived from trimethylamine N-oxide are reported as a new class of nonfouling materials., Materials that resist nonspecific protein adsorption are needed for many applications. However, few are able to achieve ultralow fouling in complex biological milieu. Zwitterionic polymers emerge as a class of highly effective ultralow fouling materials due to their superhydrophilicity, outperforming other hydrophilic materials such as poly(ethylene glycol). Unfortunately, there are only three major classes of zwitterionic materials based on poly(phosphorylcholine), poly(sulfobetaine), and poly(carboxybetaine) currently available. Inspired by trimethylamine N-oxide (TMAO), a zwitterionic osmolyte and the most effective protein stabilizer, we here report TMAO-derived zwitterionic polymers (PTMAO) as a new class of ultralow fouling biomaterials. The nonfouling properties of PTMAO were demonstrated under highly challenging conditions. The mechanism accounting for the extraordinary hydration of PTMAO was elucidated by molecular dynamics simulations. The discovery of PTMAO polymers demonstrates the power of molecular understanding in the design of new biomimetic materials and provides the biomaterials community with another class of nonfouling zwitterionic materials.
- Published
- 2019
3. A browser-based tool for visualization and analysis of diffusion MRI data
- Author
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Josh K. Smith, Adam Richie-Halford, Anisha Keshavan, Jason D. Yeatman, and Ariel Rokem
- Subjects
0301 basic medicine ,Computer science ,Science ,Big data ,General Physics and Astronomy ,Article ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,0302 clinical medicine ,lcsh:Science ,Multidisciplinary ,business.industry ,Subject (documents) ,General Chemistry ,Transparency (behavior) ,Data science ,Visualization ,Metadata ,Data sharing ,Exploratory data analysis ,030104 developmental biology ,Open source ,lcsh:Q ,business ,030217 neurology & neurosurgery - Abstract
Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable—it runs in any web-browser—it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts., Data sharing is an important component of reproducible research, but meaningful data sharing can be difficult. Here authors describe a new open source tool, AFQ-Browser, that builds an interactive website allowing visualization and exploratory data analysis of published diffusion MRI data.
- Published
- 2018
- Full Text
- View/download PDF
4. AFQ-Browser: Supporting reproducible human neuroscience research through browser-based visualization tools
- Author
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Jason D. Yeatman, Adam Richie-Halford, Josh K. Smith, Anisha Keshavan, and Ariel Rokem
- Subjects
Reproducibility ,business.industry ,Computer science ,Big data ,Subject (documents) ,Transparency (human–computer interaction) ,Data science ,Visualization ,Metadata ,Data sharing ,World Wide Web ,Exploratory data analysis ,Publishing ,Component (UML) ,business - Abstract
Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is an important component of reproducible research, it is not always clear how to usefully share data in a manner that allows other labs to understand and reproduce published findings. Here we describe a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a published diffusion MRI study. Because AFQ-browser is portable -- it runs in any modern web-browser -- it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of a diffusion MRI dataset (anatomy, quantitative diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new scientific discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, sharing and aggregating data across labs, and publishing data alongside manuscripts.
- Published
- 2017
- Full Text
- View/download PDF
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