Rensselaer Polytechnic Institute

About: Rensselaer Polytechnic Institute is a education organization based out in Troy, New York, United States. It is known for research contribution in the topics: Terahertz radiation & Population. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.

Bioactive nanoparticles stimulate bone tissue formation in bioprinted three‐dimensional scaffold and human mesenchymal stem cells

Abstract: Bioprinting based on thermal inkjet printing is a promising but unexplored approach in bone tissue engineering. Appropriate cell types and suitable biomaterial scaffolds are two critical factors to generate successful bioprinted tissue. This study was undertaken in order to evaluate bioactive ceramic nanoparticles in stimulating osteogenesis of printed bone marrow-derived human mesenchymal stem cells (hMSCs) in poly(ethylene glycol)dimethacrylate (PEGDMA) scaffold. hMSCs suspended in PEGDMA were co-printed with nanoparticles of bioactive glass (BG) and hydroxyapatite (HA) under simultaneous polymerization so the printed substrates were delivered with highly accurate placement in three-dimensional (3D) locations. hMSCs interacted with HA showed the highest cell viability (86.62 ± 6.02%) and increased compressive modulus (358.91 ± 48.05 kPa) after 21 days in culture among all groups. Biochemical analysis showed the most collagen production and highest alkaline phosphatase activity in PEG-HA group, which is consistent with gene expression determined by quantitative PCR. Masson's trichrome staining also showed the most collagen deposition in PEG-HA scaffold. Therefore, HA is more effective comparing to BG for hMSCs osteogenesis in bioprinted bone constructs. Combining with our previous experience in vasculature, cartilage, and muscle bioprinting, this technology demonstrates the capacity for both soft and hard tissue engineering with biomimetic structures.

Thermotropic mesophases and mesophase transitions of linear, flexible macromolecules

Abstract: The field of mesophases is subdivided into six different phases: liquid crystals, plastic crystals, condis crystals and the corresponding LC, PC, and CD glasses. Liquid and plastic crystals are the traditional phases with positional and orientational disorder, respectively. Condis crystals are conformationally disordered. On hand of tables of thermodynamic transition parameters of small and large molecules it is shown that the orientational order in liquid crystals is only a few per cent of the total possible, while the positional order in plastic crystals is virtually complete. Condis crystals have a wide variety of different degrees of conformational disorder. The glass transitions of all mesophases are similar in type. Macromolecules in the liquid crystalline state produce high orientation on deformation. Plastic crystals consist always of small molecules. Condis crystals may under proper conditions anneal to extended chain crystals.

Cellular automata microsimulation of bidirectional pedestrian flows

Abstract: The cellular automata (CA) microsimulation of pedestrians is a particle-hopping model in which a set of local rules prescribe the behavior of entities within local neighborhoods of cells. CA microsimulation has emerged as a tool for simulating traffic flow and modeling transportation networks. Pedestrian flow is inherently more complex than vehicular flow, and simulation models that are used for emulating vehicular traffic are not directly applicable to modeling pedestrian movements. In previous work the authors demonstrated that unidirectional pedestrian flow patterns consistent with well-established fundamental properties could be generated with CA microsimulation. This paper expands upon the previous effort and presents a CA microsimulation model and emergent fundamental flows for a bidirectional pedestrian walkway. Simulation experiments indicate that the basic model is applicable to walkways of various lengths and widths and across different directional shares of pedestrian movements.

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)

Abstract: In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.