Abstract: A training system comprises a training stand, a width, a length, a spacing measurement assembly, a training assembly, and a controller. each among a plurality of blade disks comprise a blade disk having a body portion, a center aperture, a diameter, a plurality of punched teeth, a sharpened edge, a center point, an outer edge and a center aperture diameter. portions of the plurality of blade disks comprises one or more misaligned disks and one or more nominal disks. The plurality of blade disks comprise at least a first disk and a last disk. each among the plurality of blade disks are attached to a mandrel along a center axis.

Abstract: A method of measuring efficacy of test treatment of an autoimmune disease in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. This dataset is compared to healthy animals, a standard of care or a reference treatment for the first disease to determine efficacy of the test treatment. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Other embodiment uses a Fourier transform on a circle, LASSO, RANSAC or regression analyses for curve fitting. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.

Abstract: The field of this invention is recording motion of an animal, uniquely identifying the animal, and recording an activity of that animal. Motions of animals on a path when the animal is or can be uniquely identified are tracklets. Tracklets begin and end at ambiguation events, where these are defined as locations and times of an animal where it cannot be uniquely identified. A first animal may be uniquely identified by first identifying all other animals in the first animal's environment. Animal identification may be after the end of a tracklet. Embodiments use optical flow analysis from video of an animal's environment. Embodiments record an animal's activity on tracklets and then use that activity to measure animal health or use that activity as data for a study using animals.

Abstract: A method of treating a patient for a disease, such as arthritis, is described. Steps include treating an animal for the disease with a first treatment, capturing video frames, then using optical flow to compute vector fields for each frame, then taking a maximum vector value over a first time period, then selecting a set of maximum vector values over a second time period, then clipping those values to a maximum, and then computing an average of those values. This value is an efficacy and it is compared to a baseline efficacy. If the treatment is efficacious, the patient is so treated. A method of applying for a drug for approval is also described, using substantially the same steps.

Abstract: A method of measuring efficacy of test treatment of an autoimmune disease in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. This dataset is compared to healthy animals, a standard of care or a reference treatment for the first disease to determine efficacy of the test treatment. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Other embodiment uses a Fourier transform on a circle, LASSO, RANSAC or regression analyses for curve fitting. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.

Abstract: A method of predicting severity of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. Then, an MS severity index function is applied to this dataset that, in response to the level of activity change and the speed of activity change, predicts or measures severity of MS in the animal. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.

Abstract: A method of measuring efficacy of treatment of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. This dataset is compared to healthy animals or a standard of care to determine efficacy. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.

Abstract: A method of measuring efficacy of treatment of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. This dataset is compared to healthy animals or a standard of care to determine efficacy. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.

Abstract: Systems and methods of measuring drug efficacy and side effects using non-invasive or husbandry-only testing are described. Steps include testing a cohort with a proposed husbandry-only protocol against an existing gold-standard treatment, and then validating the use of a created surrogate, non-invasive metric in place of an invasive metric. Then, the validated non-invasive surrogate metric and the husbandry-only protocols are used with an animal treatment cohort to study a new proposed treatment. A control cohort is also used, subject to the same husbandry-only testing and the surrogate metric. A statistical difference in outcomes, using one or more surrogate metrics, between the treatment cohort and the control cohort is the drug efficacy, for a drug used to treat the treatment cohort.

Abstract: The field of this invention is recording motion of an animal, uniquely identifying the animal, and recording an activity of that animal. Motions of animals on a path when the animal is or can be uniquely identified are tracklets. Tracklets begin and end at ambiguation events, where these are defined as locations and times of an animal where it cannot be uniquely identified. A first animal may be uniquely identified by first identifying all other animals in the first animal's environment. Animal identification may be after the end of a tracklet. Embodiments use optical flow analysis from video of an animal's environment. Embodiments record an animal's activity on tracklets and then use that activity to measure animal health or use that activity as data for a study using animals.

Abstract: A method of early detection of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. Then, a health detection function is applied to this dataset that, in response to the level of activity change and the speed of activity change, predicts or detects MS in the animal. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.

Abstract: A method of reading arbitrary symbols is described. Symbols may degrade over time. Shapes of symbols as they degrade are used to update an initial symbol library, which is created based on actual markings, rather than idealized symbols. Marks associate with shapes, which associate to symbols, which in aggregate associate with an object ID. A read mark is compared to all the shapes in the library to determine a most likely shape. A selection set is used to limit shape selection, based on the comparison, to shapes of valid symbols. Comparison methods use probability distributions. Confidence values are used to validate output, generate warnings, and to control updating of the library. Weighted averaging based on confidence values or age of reads may be used at the level of shapes, comparison distributions, or selections.

Abstract: A method of early detection of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. Then, a health detection function is applied to this dataset that, in response to the level of activity change and the speed of activity change, predicts or detects MS in the animal. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.

Abstract: A method of measuring animal health, such as rodent health, such as a degree of arthritis, is described. Steps include capturing video frames, then using optical flow to compute vector fields for each frame, then taking a maximum vector value over a first time period, then selecting a set of maximum vector values over a second time period, then clipping those values to a maximum, then computing an average of those values. This is an animal health metric. Metrics may be organized into a plot over time and may then be compared against known plots to compute an overall animal health and measure efficacy of treatments or other animal characteristics or behavior, including predictive.

Abstract: A method of reading degraded symbols is described. A symbol has an initial shape, which is marked on an object. Marks, shapes, symbols, and object ID are managed separately. A symbol library with symbols and associated shapes is initially created. Shapes in the library are updated from shapes of marks as the marks degrade over time. A read mark is compared to all the shapes in the library to determine a most likely shape. A selection set is used to limit symbol selection, based on the comparison, to valid symbols. The symbol library and selection set may be customized to each usage of the method. Comparison methods use probability distributions. Confidence values are used to validate output, generate warnings, and to control updating of the library. Weighted averaging may be used at the level of shapes, comparison distributions, or selections. One application is reading tattooed marks on rodent tails in a vivarium.

Abstract: A method of reading arbitrary symbols is described. Symbols may degrade over time. Shapes of symbols as they degrade are used to update an initial symbol library, which is created based on actual markings, rather than idealized symbols. Marks associate with shapes, which associate to symbols, which in aggregate associate with an object ID. A read mark is compared to all the shapes in the library to determine a most likely shape. A selection set is used to limit shape selection, based on the comparison, to shapes of valid symbols. Comparison methods use probability distributions. Confidence values are used to validate output, generate warnings, and to control updating of the library. Weighted averaging based on confidence values or age of reads may be used at the level of shapes, comparison distributions, or selections.

Abstract: A method of reading degraded symbols is described. A symbol has an initial shape, which is marked on an object. Marks, shapes, symbols, and object ID are managed separately. A symbol library with symbols and associated shapes is initially created. Shapes in the library are updated from shapes of marks as the marks degrade over time. A read mark is compared to all the shapes in the library to determine a most likely shape. A selection set is used to limit symbol selection, based on the comparison, to valid symbols. The symbol library and selection set may be customized to each usage of the method. Comparison methods use probability distributions. Confidence values are used to validate output, generate warnings, and to control updating of the library. Weighted averaging may be used at the level of shapes, comparison distributions, or selections. One application is reading tattooed marks on rodent tails in a vivarium.

Abstract: A method of predicting severity of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. Then, an MS severity index function is applied to this dataset that, in response to the level of activity change and the speed of activity change, predicts or measures severity of MS in the animal. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.

Abstract: A method of measuring efficacy of treatment of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. This dataset is compared to healthy animals or a standard of care to determine efficacy. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.

Abstract: A method of early detection of multiple sclerosis (MS) in an animal in a vivarium is described. Animal activity data is collected at multiple times during the night. Sequential time regions of the night are identified as high-activity, activity-drop, or low-activity regions. Embodiments are described to quantify a drop, during the night, of an animal's activity level. These quantified activity-drop scalars for consecutive nights are accumulated in an animal health dataset. Then, a health detection function is applied to this dataset that, in response to the level of activity change and the speed of activity change, predicts or detects MS in the animal. One embodiment quantifies an activity-drop by fitting straight-line curves through the data in the three nightly regions. Another embodiment uses a Fourier transform on a circle and a linear combination. Another embodiment compares areas under data curves in the regions. Animals may be housed in cages with other animals.