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.

On the Formation and Occurrence of Saddle Dolomite

Abstract: Saddle dolomite is a variety of dolomite that has a warped crystal lattice; it is characterized by curved crystal faces and cleavage, and sweeping extinction. Perfect saddle forms have trigonal symmetry, with crystal elongation at high angles to the 'c' axis. Saddle dolomite occurs as both a void-filling cement and a replacement mineral and is commonly associated with hydrocarbons, epigenetic base-metal mineralization, and sulfate-rich carbonates. These associations imply late diagenetic formation by sulfate reduction processes. Saddle dolomite is slightly enriched in calcium and has significant variations in composition within individual growth laminae. Calcium is more abundant in the lattice at crystal apices and face edges that are at high angles to the 'c' axis, than towards face centers. These composition gradients along growth laminae cause the lattice distortion which has trigonal symmetry corresponding to the saddle morphology. The cause of selective ion adsorption during crystal growth is open to speculation but must be associated with the crystal as an entity. Surface-charge effects, the most probable cause, may be produced by either a pyro-electric phenomenon at elevated temperatures or pH and ionic concentrations of the precipitating fluids. Saddle dolomite has potential as a geothermometer, being indicative of elevated temperatures (60-150°C).

Chemoenzymatic Synthesis of Homogeneous Ultralow Molecular Weight Heparins

Abstract: Ultralow molecular weight (ULMW) heparins are sulfated glycans that are clinically used to treat thrombotic disorders. ULMW heparins range from 1500 to 3000 daltons, corresponding from 5 to 10 saccharide units. The commercial drug Arixtra (fondaparinux sodium) is a structurally homogeneous ULMW heparin pentasaccharide that is synthesized through a lengthy chemical process. Here, we report 10- and 12-step chemoenzymatic syntheses of two structurally homogeneous ULMW heparins (MW = 1778.5 and 1816.5) in 45 and 37% overall yield, respectively, starting from a simple disaccharide. These ULMW heparins display excellent in vitro anticoagulant activity and comparable pharmacokinetic properties to Arixtra, as demonstrated in a rabbit model. The chemoenzymatic approach is scalable and shows promise for a more efficient route to synthesize this important class of medicinal agent.

3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network

Abstract: Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN) architectures. This paper introduces a conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising. A novel feature of this approach is that an initial 3-D CPCE denoising model can be directly obtained by extending a trained 2-D CNN, which is then fine-tuned to incorporate 3-D spatial information from adjacent slices. Based on the transfer learning from 2-D to 3-D, the 3-D network converges faster and achieves a better denoising performance when compared with a training from scratch. By comparing the CPCE network with recently published work based on the simulated Mayo data set and the real MGH data set, we demonstrate that the 3-D CPCE denoising model has a better performance in that it suppresses image noise and preserves subtle structures.