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 & Finite element method. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.

Mechanism of Action of Nalidixic Acid on Escherichia coli II. Inhibition of Deoxyribonucleic Acid Synthesis

Abstract: Goss, William A. (Sterling-Winthrop Research Institute, Rensselaer, N.Y.), William H. Deitz, and Thomas M. Cook. Mechanism of action of nalidixic acid on Escherichia coli. II. Inhibition of deoxyribonucleic acid synthesis. J. Bacteriol. 89:1068-1074. 1965.-Nalidixic acid was shown to inhibit specifically the synthesis of deoxyribonucleic acid (DNA) in Escherichia coli. Slight effects on protein and ribonucleic acid (RNA) synthesis were observed only at higher levels of drug or after prolonged incubation. The inhibition of DNA synthesis in E. coli 15TAU, as measured by incorporation of C(14)-labeled thymine, was observed after exposure to nalidixic acid for 10 min. Inhibition of the incorporation of C(14)-labeled uracil into RNA and C(14)-labeled l-arginine into protein (21 and 28% inhibition, respectively) was observed only after 60 min of exposure. When cultures of E. coli 15TAU were exposed to 3.0 mug/ml of nalidixic acid (slightly greater than the minimal growth inhibitory concentration), the incorporation of C(14)-labeled thymidine was inhibited 30 to 40% after 90 min. Nalidixic acid at 10 mug/ml, a lethal concentration, inhibited thymidine incorporation 72% during this period. Nalidixic acid at 1.0 mug/ml had no apparent effect on the incorporation of C(14)-labeled adenine or C(14)-labeled uracil into RNA of cultures of E. coli 198, a wild-type strain. However, incorporation of both bases into DNA was strongly inhibited after 60 min of exposure (66 and 69%, respectively). Nalidixic acid inhibited DNA replication during a single round of synthesis. In contrast with "thymineless death," nalidixic acid was not lethal to E. coli 15TAU during restricted RNA and protein synthesis (i.e., in a medium containing thymine but lacking arginine and uracil). We have shown also that this chemotherapeutic agent has little effect on the synthesis of protein or RNA required to initiate DNA replication. After 75 min of inhibition, the capacity of E. coli 15TAU to synthesize DNA in a medium containing thymine, arginine, and uracil may be restored by a simple filtration and washing process, indicating that the drug is not firmly bound. These studies leave little doubt that a primary action of nalidixic acid is the inhibition of the synthesis of DNA in E. coli.

T-ray computed tomography.

Abstract: We demonstrate a tomographic imaging modality that uses pulsed terahertz (THz) radiation to probe the optical properties of three-dimensional (3D) structures in the far-infrared. This THz-wave computed tomography (T-ray CT) system provides sectional images of objects in a manner analogous to conventional CT techniques such as x-ray CT. The transmitted amplitude and phase of broadband pulses of THz radiation are measured at multiple projection angles. The filtered backprojection algorithm is then used to reconstruct the target object, including both its 3D structure and its frequency-dependent far-infrared optical properties.

LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT

Abstract: Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view computed tomography (CT), tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly uses regularizers in the CS framework. Currently, how to choose the parameters adaptively for regularization is a major open problem. In this paper, inspired by the idea of machine learning especially deep learning, we unfold the state-of-the-art “fields of experts”-based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a learned experts’ assessment-based reconstruction network (LEARN) for sparse-data CT, and demonstrate the feasibility and merits of our LEARN network. The experimental results with our proposed LEARN network produces a superior performance with the well-known Mayo Clinic low-dose challenge data set relative to the several state-of-the-art methods, in terms of artifact reduction, feature preservation, and computational speed. This is consistent to our insight that because all the regularization terms and parameters used in the iterative reconstruction are now learned from the training data, our LEARN network utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms. Also, the number of layers in the LEARN network is only 50, reducing the computational complexity of typical iterative algorithms by orders of magnitude.