Abstract: A method to develop one or more morphometric classifiers to identify a tumor mutation burden (TMB). The method provides a non-invasive method of characterizing TMB that is responsive to a tumor in its early stages of development and irrespective of the tumor size. The method allows targeting cancer therapy to the specific characteristics of the cancer that the patient may have, allowing more efficient cancer management with far fewer side effects.

Abstract: A method to develop one or more morphometric classifiers to identify a mismatch repair deficiency (MMRD). The method provides a non-invasive method of characterizing MMRD that is responsive to a tumor in its early stages of development and irrespective of the tumor size. The method allows targeting cancer therapy to the specific characteristics of the cancer that the patient may have, allowing more efficient cancer management with far fewer side effects.

Abstract: The present disclosure provides a system and method for AI-based cell classification of cells from a patient sample to determine if cells indicative of lung cancer are present. In the system and method, 2D imaging is used to eliminate cells not likely to be indicative of lung cancer from subsequent 3D imaging, while 3D imaging is conducted for cells likely to be indicative of lung cancer. The present disclosure further provides a method of training 2D cell classifiers for use in the system and method for AI-based cell classification.

Abstract: A method to develop one or more morphometric classifiers to identify a tumor mutation burden (TMB). The method provides a non-invasive method of characterizing TMB that is responsive to a tumor in its early stages of development and irrespective of the tumor size. The method allows targeting cancer therapy to the specific characteristics of the cancer that the patient may have, allowing more efficient cancer management with far fewer side effects.

Abstract: A method for enhancing gating performance of a cell sorter to prepare an enriched specimen for optical tomography cell analysis includes introducing a specimen into a FACS to generate 2D event data; generating a first scatterplot of the 2D data; identifying target objects; constructing a boundary within the first scatterplot to produce a first gate; counting target objects within the first gate; comparing the number of target objects within the first gate to a first predetermined value and adjusting the first gate as necessary. A boundary around a set of target objects is constructed in a second scatterplot to produce a subset second gate and target objects within the second gate are counted and the count compared to a second predetermined value. When a boundary around target objects meets specifications the first and second gates are stored in memory and used to enrich patient specimens.

Abstract: A method for enhancing gating performance of a cell sorter to prepare an enriched specimen for optical tomography cell analysis includes introducing a specimen into a FACS to generate 2D event data; generating a first scatterplot of the 2D data; identifying target objects; constructing a boundary within the first scatterplot to produce a first gate; counting target objects within the first gate; comparing the number of target objects within the first gate to a first predetermined value and adjusting the first gate as necessary. A boundary around a set of target objects is constructed in a second scatterplot to produce a subset second gate and target objects within the second gate are counted and the count compared to a second predetermined value. When a boundary around target objects meets specifications the first and second gates are stored in memory and used to enrich patient specimens.

Abstract: A method to develop one or more morphometric classifiers to identify a tumor mutation burden (TMB). The method provides a non-invasive method of characterizing TMB that is responsive to a tumor in its early stages of development and irrespective of the tumor size. The method allows targeting cancer therapy to the specific characteristics of the cancer that the patient may have, allowing more efficient cancer management with far fewer side effects.

Abstract: A cell classification system includes an optical tomography system and a processor operable to generate a plurality of 2D images and a plurality of pseudo-projection images of a cell. The processor executes instructions that cause the processor to: generate a 3D image of the cell using the pseudo-projection images; apply digital enhancement to a 2D or 3D image to improve determination of boundaries of structures with the cell that provide indicia of cell features; analyze at least one of the cell features or 2D or 3D images using AI-based cell characterization to characterize the cell as normal or as having abnormal features by analyzing the boundaries of structures; create a Normal Cell Gallery comprising images characterized by AI-based characterization as normal; create a Diagnostic Cell Gallery with images characterized by AI-based characterization as having abnormal features; display at least the Diagnostic Cell Gallery; and record a user classification.

Abstract: An aircraft includes a duct system configured to receive a flow of air therethrough and a gas turbine engine. The duct system includes a main duct and inlet ducts arranged fluidly upstream of the main duct so as to conduct the flow of air from the inlet ducts into the main duct. The gas turbine engine in downstream fluid communication with the main duct.

Abstract: A gas turbine engine comprises a primary fan and an engine core. The primary fan is mounted for rotation about an axis of the gas turbine engine to provide thrust. The engine core is coupled to the primary fan and configured to drive the primary fan about the axis to cause the fan to push air to provide thrust for the gas turbine engine.

Abstract: An aircraft includes a duct system configured to receive a flow of air therethrough and a gas turbine engine. The duct system includes a main duct and inlet ducts arranged fluidly upstream of the main duct so as to conduct the flow of air from the inlet ducts into the main duct. The gas turbine engine in downstream fluid communication with the main duct.

Abstract: A gas turbine engine comprises a primary fan and an engine core. The primary fan is mounted for rotation about an axis of the gas turbine engine to provide thrust. The engine core is coupled to the primary fan and configured to drive the primary fan about the axis to cause the fan to push air to provide thrust for the gas turbine engine.

Abstract: The present disclosure provides a system and method for AI-based cell classification of cells from a patient sample to determine if cells indicative of lung cancer are present. In the system and method, 2D imaging is used to eliminate cells not likely to be indicative of lung cancer from subsequent 3D imaging, while 3D imaging is conducted for cells likely to be indicative of lung cancer. The present disclosure further provides a method of training 2D cell classifiers for use in the system and method for AI-based cell classification.

Abstract: A gas turbine engine comprises a primary fan and an engine core. The primary fan is mounted for rotation about an axis of the gas turbine engine to provide thrust. The engine core is coupled to the primary fan and configured to drive the primary fan about the axis to cause the fan to push air to provide thrust for the gas turbine engine.

Abstract: A gas turbine engine comprises a primary fan and an engine core. The primary fan is mounted for rotation about an axis of the gas turbine engine to provide thrust. The engine core is coupled to the primary fan and configured to drive the primary fan about the axis to cause the fan to push air to provide thrust for the gas turbine engine.

Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

Abstract: A gas turbine engine comprises an engine core and an electric fan array. The engine core includes a compressor, a combustor, and a turbine. The compressor compresses and delivers air to the combustor. The combustor mixes fuel with the compressed air received from the compressor and ignites the fuel. The hot, high-pressure products of the combustion reaction in the combustor are directed into the turbine to cause the turbine to rotate about an axis and drive the compressor. The electric fan array may include at least two non-concentric fans having parallel shafts.

Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.