Patent application number | Description | Published |
20100323381 | CLASSIFYING AMYLOIDOSIS - This document provides methods and materials related to determining the identity of amyloid polypeptides in biological samples. For examples, methods and materials for identifying an amyloid polypeptide present in a fat aspirate are provided herein. | 12-23-2010 |
20100324119 | REDUCING IRF4, DUSP22, OR FLJ43663 POLYPEPTIDE EXPRESSION - This document relates to the activity of interferon regulatory factor 4 (IRF4) in T-cell lymphomas. For example, methods and materials involved in reducing the expression of an IRF4 polypeptide in T-cell lymphoma cells and identifying agents having the ability to reduce expression of an IRF4 polypeptide in T-cell lymphoma cells are provided. This document also relates to reducing DUSP22 or FLJ43663 polypeptide activity in T-cell lymphomas. For example, methods and materials involved in reducing the expression of DUSP22 polypeptides and/or FLJ43663 polypeptides in T-cell lymphoma cells and identifying agents having the ability to reduce expression of DUSP22 polypeptides and/or FLJ43663 polypeptides in T-cell lymphoma cells are provided. | 12-23-2010 |
20110306517 | REDUCING IRF4, DUSP22, OR FLJ43663 POLYPEPTIDE EXPRESSION - This document relates to the activity of interferon regulatory factor 4 (IRF4) in T-cell lymphomas. For example, methods and materials involved in reducing the expression of an IRF4 polypeptide in T-cell lymphoma cells and identifying agents having the ability to reduce expression of an IRF4 polypeptide in T-cell lymphoma cells are provided. This document also relates to reducing DUSP22 or FLJ43663 polypeptide activity in T-cell lymphomas. For example, methods and materials involved in reducing the expression of DUSP22 polypeptides and/or FLJ43663 polypeptides in T-cell lymphoma cells and identifying agents having the ability to reduce expression of DUSP22 polypeptides and/or FLJ43663 polypeptides in T-cell lymphoma cells are provided. | 12-15-2011 |
Patent application number | Description | Published |
20090261847 | METHOD FOR DETERMINING THE DIELECTRIC CONSTANT OF PARTICLES - A method of measuring the dielectric constant of a powder, including selecting a powder having an unknown first dielectric constant, selecting a liquid having a known second dielectric constant, and introducing a predetermined amount of powder into a predetermined volume of liquid to define a slurry characterized by a known volume fraction of powder. Next, the impedance spectra of the slurry is plotted over a predetermined frequency range, the measured dielectric constant data is read and the appropriate equivalent circuit for the slurry is determined. Appropriate equivalent circuit equations are applied to the measured dielectric constant data and the first dielectric constant is calculated from the appropriate equivalent circuit equations, known volume fraction of powder and measured dielectric constant data. | 10-22-2009 |
20110051315 | NANOSTRUCTURED DIELECTRIC MATERIALS FOR HIGH ENERGY DENSITY MULTI LAYER CERAMIC CAPACITORS - A high energy density multilayer ceramic capacitor, having at least two electrode layers and at least one substantially dense polycrystalline dielectric layer positioned therebetween. The at polycrystalline dielectric layer has an average grain size of less than about 300 nanometers, a particle size distribution of between about 150 nanometers and about 3 micrometers, and a maximum porosity of about 1 percent. The dielectric layer is selected from the group including TiO | 03-03-2011 |
20130037998 | NANOSTRUCTURED DIELECTRIC MATERIALS FOR HIGH ENERGY DENSITY MULTI LAYER CERAMIC CAPACITORS - A high energy density multilayer ceramic capacitor, having at least two electrode layers and at least one substantially dense polycrystalline dielectric layer positioned therebetween. The at polycrystalline dielectric layer has an average grain size of less than about 300 nanometers, a particle size distribution of between about 150 nanometers and about 3 micrometers, and a maximum porosity of about 1 percent. The dielectric layer is selected from the group including TiO | 02-14-2013 |
20130050900 | NANOSTRUCTURED DIELECTRIC MATERIALS FOR HIGH ENERGY DENSITY MULTI LAYER CERAMIC CAPACITORS - A high energy density multilayer ceramic capacitor, having at least two electrode layers and at least one substantially dense polycrystalline dielectric layer positioned therebetween. The at polycrystalline dielectric layer has an average grain size of less than about 300 nanometers, a particle size distribution of between about 150 nanometers and about 3 micrometers, and a maximum porosity of about 1 percent. The dielectric layer is selected from the group including TiO | 02-28-2013 |
20130063858 | NANOSTRUCTURED DIELECTRIC MATERIALS FOR HIGH ENERGY DENSITY MULTILAYER CERAMIC CAPACITORS - A multilayer ceramic capacitor, having a plurality of electrode layers and a plurality of substantially titanium dioxide dielectric layers, wherein each respective titanium dioxide dielectric layer is substantially free of porosity, wherein each respective substantially titanium dioxide dielectric layer is positioned between two respective electrode layers, wherein each respective substantially titanium dioxide dielectric layer has an average grain size of between about 200 and about 400 nanometers, wherein each respective substantially titanium dioxide dielectric layer has maximum particle size of less than about 500 nanometers. Typically, each respective substantially titanium dioxide dielectric layer further includes at least one dopant selected from the group including P, V, Nb, Ta, Mo, W, and combinations thereof, and the included dopant is typically present in amounts of less than about 0.01 atomic percent. | 03-14-2013 |
Patent application number | Description | Published |
20090177512 | Determining A Policy Parameter For An Entity Of A Supply Chain - Determining a policy parameter for an entity of a supply chain includes establishing attributes of the entities of the supply chain. Attribute segments are established for each attribute, where an attribute segment includes one or more values of the corresponding attribute. Rules are formulated using the attribute segments to define policy groups, and policy parameters are assigned to each policy group. A policy group corresponding to an entity is identified in accordance with the rules. The policy parameters assigned to the identified policy group are determined and selected for the entity. | 07-09-2009 |
20120158457 | Determining an Inventory Target for a Node of a Supply Chain - Determining an inventory target for a node of a supply chain includes calculating a demand stock for satisfying a demand over supply lead time at the node of the supply chain, and calculating a demand variability stock for satisfying a demand variability of the demand over supply lead time at the node. A demand bias of the demand at the node is established. An inventory target for the node is determined based on the demand stock and the demand variability stock in accordance with the demand bias. | 06-21-2012 |
20130262177 | Determining an Inventory Target for a Node of a Supply Chain - Determining an inventory target for a node of a supply chain includes calculating a demand stock for satisfying a demand over supply lead time at the node of the supply chain, and calculating a demand variability stock for satisfying a demand variability of the demand over supply lead time at the node. A demand bias of the demand at the node is established. An inventory target for the node is determined based on the demand stock and the demand variability stock in accordance with the demand bias. | 10-03-2013 |
20150134397 | Determining an Inventory Target for a Node of a Supply Chain - Determining an inventory target for a node of a supply chain includes calculating a demand stock for satisfying a demand over supply lead time at the node of the supply chain, and calculating a demand variability stock for satisfying a demand variability of the demand over supply lead time at the node. A demand bias of the demand at the node is established. An inventory target for the node is determined based on the demand stock and the demand variability stock in accordance with the demand bias. | 05-14-2015 |