Abstract: A focusing distance (e.g., a distance between a focal plane of a camera used when n image was captured and the actual focal plane for an in-focus image) in a visual analysis system may be determined by subjecting one or more images captured by such a system to a multi-layer analysis. In such an analysis, an input may be subjected to one or more convolution filters, and the ultimate result of such convolution may be provided to a dense layer which can provide the focusing distance. This focusing distance may then be used to (re) focus a camera or for other purposes (e.g., generating an alert).

Abstract: A focusing distance (e.g., a distance between a focal plane of a camera used when n image was captured and the actual focal plane for an in-focus image) in a visual analysis system may be determined by subjecting one or more images captured by such a system to a multi-layer analysis. In such an analysis, an input may be subjected to one or more convolution filters, and the ultimate result of such convolution may be provided to a dense layer which can provide the focusing distance. This focusing distance may then be used to (re)focus a camera or for other purposes (e.g., generating an alert).

Abstract: An analyzer may have its operation validated using control samples which comprise synthetic particles. Such an analyzer be adapted to obtain a representation of a particle from a sample and obtain a classification of the particle which classifies the particle as a type specific to control samples. The analyzer may also be adapted to perform control specific processing on the sample, and to generate an analysis output for the sample based on a result of the control specific processing.

Abstract: A visual analysis system may be automatically focused (or the focus of such a system may be automatically corrected) by subjecting one or more images captured by such a system to a multi-layer analysis. In such an analysis, a cell boundary may be identified in an input image based on the lightness value of the pixels of the input image. Based on the identified cell boundary, a predicted nominal focus value is determined which can provide a focusing distance (e.g., a distance between the focal plane of a camera used when an image was captured and the actual focal plane for an in-focus image). This focusing distance may then be used to (re) focus a camera or for other purposes (e.g., generating an alert).

Abstract: A focusing distance (e.g., a distance between a focal plane of a camera used when n image was captured and the actual focal plane for an in-focus image) in a visual analysis system may be determined by subjecting one or more images captured by such a system to a multi-layer analysis. In such an analysis, an input may be subjected to one or more convolution filters, and the ultimate result of such convolution may be provided to a dense layer which can provide the focusing distance. This focusing distance may then be used to (re)focus a camera or for other purposes (e.g., generating an alert).

Abstract: Systems and methods of assessing a probability that an individual will develop sepsis are provided. The systems and methods can include obtaining a set of parameters associated with the individual including white blood cell count (WBC) and monocyte distribution width (MDW) value, and determining whether the set of parameters provides an elevated risk status by comparing at least the WBC and the MDW value with respective predetermined criteria. In the event that the set of parameters is determined to provide the elevated risk status, the systems and methods can further include obtaining a secondary parameter associated with the individual; and providing the probability that the individual will develop sepsis.

Abstract: A method for dynamic focusing is presented that can be performed by a dynamic focusing controller that can receive images from the image capture device, and for each image, determine a border of the particle within the image, and calculate a pixel intensity ratio of the image based on the border of the particle. The dynamic focusing controller can also calculate a median pixel intensity ratio from the pixel intensity ratios for each image, determine a focal distance direction based on the median pixel intensity ratio, calculate a focal distance based on the median pixel intensity ratio when the focal distance direction is positive, and calculate the focal distance based on a median border width when the focal distance direction is negative. The autofocusing controller can then send an instruction to the focusing mechanism to adjust the image capture device by the focal distance in the focal distance direction.

Abstract: A method for dynamic focusing is presented that can be performed by a dynamic focusing controller that can receive images from the image capture device, and for each image, determine a border of the particle within the image, and calculate a pixel intensity ratio of the image based on the border of the particle. The dynamic focusing controller can also calculate a median pixel intensity ratio from the pixel intensity ratios for each image, determine a focal distance direction based on the median pixel intensity ratio, calculate a focal distance based on the median pixel intensity ratio when the focal distance direction is positive, and calculate the focal distance based on a median border width when the focal distance direction is negative. The autofocusing controller can then send an instruction to the focusing mechanism to adjust the image capture device by the focal distance in the focal distance direction.

Abstract: Container identification data from a container inspection unit that analyzes a container containing a liquid is combined with liquid level detection raw data from a liquid level detection unit that analyzes the container containing the liquid and a liquid level detection result is generated. The liquid level detection result is cross-checked with additional data from the container inspection unit. The result can be used to plan a route for the container in the laboratory automation system.

Abstract: Methods and systems for automatically identifying and enumerating early granulated cells (EGC) in blood samples are disclosed. In one embodiment a method for identifying EGC in a blood sample includes analyzing white blood cells of the blood sample using a low angle light scatter (LALS) parameter, separating the EGCs from the other white blood cells using the LALS parameter, and enumerating the separated EGCs.

Abstract: Embodiments of the present invention encompass automated systems and methods for analyzing immature platelet parameters in an individual based on a biological sample obtained from blood of the individual. Exemplary techniques involve correlating aspects of direct current (DC) impedance, radiofrequency (RF) conductivity, and/or light measurement data obtained from the biological sample with an evaluation of immature platelet conditions in the individual.

Abstract: This invention provides combinations of biomarkers for diagnosis of obstructive sleep apnea.

Abstract: Embodiments of the present invention encompass automated systems and methods for analyzing immature platelet parameters in an individual based on a biological sample obtained from blood of the individual. Exemplary techniques involve correlating aspects of direct current (DC) impedance, radiofrequency (RF) conductivity, and/or light measurement data obtained from the biological sample with an evaluation of immature platelet conditions in the individual.

Abstract: Methods and systems for automatically identifying and enumerating early granulated cells (EGC) in blood samples are disclosed. In one embodiment a method for identifying EGC in a blood sample includes analyzing white blood cells of the blood sample using a low angle light scatter (LALS) parameter, separating the EGCs from the other white blood cells using the LALS parameter, and enumerating the separated EGCs.

Abstract: Methods and systems for automatically identifying and enumerating early granulated cells (EGC) in blood samples are disclosed. In one embodiment a method for identifying EGC in a blood sample includes analyzing white blood cells of the blood sample using a low angle light scatter (LALS) parameter, separating the EGCs from the other white blood cells using the LALS parameter, and enumerating the separated EGCs.

Abstract: A method for determining the presence of a biological entity. The method may include entering into a digital computer, at least a plurality of first input values associated with a first genetic element, a plurality of second input values associated with a second genetic element, and a plurality of third input values associated with a third genetic element associated with a plurality of samples. The method also includes determining a threshold value associated with the third genetic element, separating the samples using the threshold value into a first set of samples and a second set of samples, clustering the first set of samples in a feature space defined by the first genetic element and the second genetic element, defining a first boundary space using the first set of samples, and defining a second boundary space using the second set of samples.

Abstract: Embodiments of the present invention encompass automated systems and methods for analyzing white blood cell parameters in an individual based on a biological sample obtained from blood of the individual. Exemplary techniques involve correlating aspects of direct current (DC) impedance, radiofrequency (RF) conductivity, and/or light measurement data obtained from the biological sample with an evaluation of white blood cell conditions in the individual.

Abstract: A method for determining the presence of a biological entity. The method may include entering into a digital computer, at least a plurality of first input values associated with a first genetic element, a plurality of second input values associated with a second genetic element, and a plurality of third input values associated with a third genetic element associated with a plurality of samples. The method also includes determining a threshold value associated with the third genetic element, separating the samples using the threshold value into a first set of samples and a second set of samples, clustering the first set of samples in a feature space defined by the first genetic element and the second genetic element, defining a first boundary space using the first set of samples, and defining a second boundary space using the second set of samples.

Abstract: Systems and methods for identifying Methicillin resistant strains of Staphylococcus aureus (MRSA) in a sample are used that are based on the fact that an MRSA positive sample should have roughly the same copy numbers of mecA, SCCmec, and a Staphylococcus aureus-specific target gene sequence. The systems and methods may further present the three assays simultaneously on a 2-D plot with each axis of the plot 120 degrees apart. According to one embodiment, a Y plot is used for the 2-D display. If a given sample has similar readings of mecA, SCCmec, and a Staphylococcus aureus-specific target gene sequence, the sample's measured copy numbers of mecA, SCCmec, and the Staphylococcus aureus-specific target gene sequence can plot close to the origin regardless of the sample's absolute assay readings. With the help of this transformation, a boundary function can be defined that can be used to distinguish MRSA-positive samples from MRSA-negative samples.

Abstract: Container identification data from a container inspection unit that analyzes a container containing a liquid is combined with liquid level detection raw data from a liquid level detection unit that analyzes the container containing the liquid and a liquid level detection result is generated. The liquid level detection result is cross-checked with additional data from the container inspection unit. The result can be used to plan a route for the container in the laboratory automation system.