Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Computer science & Cognitive radio. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier

Abstract: The World Health Organization (WHO) recently reported that the lung tumor was the leading cause of death worldwide. In this study, a practical computer-aided diagnosis (CAD) system is developed to increase a patient's chance of survival. Segmentation is acritical analysis tool for dividing a lung image into several sub-regions. This work characterized an automated 3-D lung segmentation tool modeled by an active contour model for computed tomography (CT) images. The proposed segmentation model is used to integrate the local image bias field formulation with the active contour model (ACM). Here, a local energy term is specified by using the mean squared error to reconcile severely in homogeneous CT images and used to detect and segment tumor regions efficiently with intensity inhomogeneity. In addition, a Multiscale Gaussian distribution was applied to the CT images for smoothening the evolution process, and features were determined. For proposed model evaluation, were used the Lung Image Database Consortium (LIDC-IDRI) data set that consisted of 850 lung nodule-lesion images that were segmented and refined to generate accurate 3D lesions of lung tumor CT images. Tumor portions were extracted with 97% accuracy. Using continuous feature extraction of 3-D images leads to attributing the deformation and quantifies the centroid displacement. In this work, predict the centroid displacement and contour points by a curve evolution method which results in more accurate predictions of contour changes and than the extracted images were classified using an Enhanced Convolutional Neural Network (CNN) Classifier. The experimental result shows that the modified Computer Aided Diagnosis (CAD) system has a high ability to acquire good accuracy and assures automated diagnosis of a lung tumor.

Defining recovery neurobiology of injured spinal cord by synthetic matrix-assisted hMSC implantation.

Abstract: Mesenchymal stromal stem cells (MSCs) isolated from adult tissues offer tangible potential for regenerative medicine, given their feasibility for autologous transplantation. MSC research shows encouraging results in experimental stroke, amyotrophic lateral sclerosis, and neurotrauma models. However, further translational progress has been hampered by poor MSC graft survival, jeopardizing cellular and molecular bases for neural repair in vivo. We have devised an adult human bone marrow MSC (hMSC) delivery formula by investigating molecular events involving hMSCs incorporated in a uniquely designed poly(lactic-co-glycolic) acid scaffold, a clinically safe polymer, following inflammatory exposures in a dorsal root ganglion organotypic coculture system. Also, in rat T9–T10 hemisection spinal cord injury (SCI), we demonstrated that the tailored scaffolding maintained hMSC stemness, engraftment, and led to robust motosensory improvement, neuropathic pain and tissue damage mitigation, and myelin preservation. The scaffolded nontransdifferentiated hMSCs exerted multimodal effects of neurotrophism, angiogenesis, neurogenesis, antiautoimmunity, and antiinflammation. Hindlimb locomotion was restored by reestablished integrity of submidbrain circuits of serotonergic reticulospinal innervation at lumbar levels, the propriospinal projection network, neuromuscular junction, and central pattern generator, providing a platform for investigating molecular events underlying the repair impact of nondifferentiated hMSCs. Our approach enabled investigation of recovery neurobiology components for injured adult mammalian spinal cord that are different from those involved in normal neural function. The uncovered neural circuits and their molecular and cellular targets offer a biological underpinning for development of clinical rehabilitation therapies to treat disabilities and complications of SCI.

Fluorescence correlation spectroscopy studies of diffusion of a weak polyelectrolyte in aqueous solutions.

Abstract: We apply fluorescent correlation spectroscopy (FCS) to investigate solution dynamics of a synthetic polyelectrolyte, i.e., a weak polycarboxylic acid in aqueous solutions. The technique brings single molecule sensitivity and molecular specificity to dynamic measurements of polyelectrolyte solutions. Translational diffusion of Alexa-labeled poly(methacrylic acid), PMAA*, chains was studied in very dilute, 10−4 mg/ml, solutions as a function of solution pH and ionic strength. The observed changes in diffusion coefficients were consistent with about twofold expansion of PMAA* coils when pH was changed from 5 to 8, and with chain contraction for alkaline metal ion concentrations from 0.01 to 0.1 M. The dependence of the hydrodynamic size of PMAA* chains on the counterion type followed the sequence: Li+>Na+≈Cs+>K+. The dependence of translational diffusion on polyacid concentration was weak at the low concentration limit, but chain motions were significantly slower at higher polymer concentrations when PMAA chains overlapped. Finally, measurements of dynamics of PMAA* chains in “salt-free” solutions showed that self-diffusion of PMAA* chains significantly slowed down when PMAA concentration was increased, probably reflecting the sensitivity of PMAA* translational motions to the onset of interchain domain formation. These results illustrate the utility of the FCS technique for studying hydrodynamic sizes of polyelectrolyte coils in response to variation in solution pH or concentration of salt and polyelectrolytes. They also suggest that FCS will be a promising technique for selective observation of the dynamics of polyelectrolyte components in complex polymer mixtures.