Abstract: Techniques are disclosed herein for improving the accuracy of test data obtained outside of a clinical setting. Using the technologies described herein, different techniques can be utilized to analyze, score and adjust test data associated with one or more “at home” tests. In some examples, computing systems are utilized to generate quality scores indicating the accuracy of the test data associated with a particular biomarker. In other examples, an authorized user, such as a data manager can analyze the test data utilizing a user interface to generate scores and/or adjust the test data.

Abstract: Techniques are disclosed herein for generating personalized food guidance using predicted food responses. Using the technologies described herein, instead of providing food guidance that is generalized for a group of individuals, personalized food guidance is provided that takes into account an individual's personalized responses to foods, such as the individual's glucose and fat responses, the individual's micro biome, the individual's overall health, potential health risks for the individual, and/or other data associated with the individual. Providing an individual with personalized food guidance can make choosing food easier and healthier.

Abstract: Techniques are disclosed herein for generating predictions of gut microbiome diversity for a user. In some examples, a nutritional service may utilize data associated with a gut transit time and user data to generate a prediction of gut microbiome diversity for a user. For example, the nutritional service may perform an analysis of the data associated with gut transit time and the answers to questions to generate the prediction. In some examples, the nutritional service identifies a uniqueness of the microbiome, identify interesting species, and the like. The information determined and/or otherwise generated by the nutritional service may be presented to the user on a display.

Abstract: Techniques are disclosed herein for generating personalized glucose ranges. In some examples, during a calibration period, a user may consume foods that they normally consume, as well as some standardized meals. Personalized upper limits and lower limits to the glucose range can be determined (e.g., using machine learning techniques) that account for the shape of glucose trace for the particular user, the food consumed during the calibration period, as well as other non-glucose factors such as questionnaires, the user's lipid profile, obesity or other health risks, and the like. In some cases, food recommendations may also be provided to the user (e.g., using machine learning techniques). The personalized glucose range and other information may be presented to the user on a display.

Abstract: Techniques are disclosed herein for improving the accuracy of nutritional responses measured in a non-clinical setting. Using the technologies described herein, different techniques can be utilized to improve the accuracy of test data associated with one or more “at home” tests. In some examples, more than one test is utilized to improve the accuracy of test data associated with a particular biomarker. In other examples, a data accuracy service can programmatically analyze data received from an individual and determine whether the data is accurate. In some examples, a computing device is utilized to assist in determining what food item(s) are consumed, as well as determine whether a test protocol was followed.

Abstract: Techniques are disclosed herein for using standardized meals and digital devices can be utilized outside a clinical setting to assist in determining/predicting one or more clinical states associated with one or more individuals. Using the technologies described herein, different techniques can be utilized to for using nutritional response measurements. For example, response measurements can be obtained for two fat meals instead of a single fat meal. Data associated with a circadian rhythm (e.g., sleep times, awake times) can also be utilized to improve the accuracy of the measured biomarkers. In other examples, biome data and other data can be utilized.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.

Abstract: Techniques are disclosed herein for generating microbiome fingerprints, dietary fingerprints, and microbiome ancestry. Microbiome data associated with an individual is analyzed to generate a microbiome fingerprint, and a dietary fingerprint for a user. A “microbiome fingerprint” uniquely identifies the microbiome of a user at a particular point in time and is based on a combination of different profiles generated from the microbiome data. The dietary fingerprint identifies how the microbiome of a user is associated with one or more different indexes, such as a dietary index and/or a particular characteristic (e.g., a Mediterranean diet index, a vegetarian diet index, a fast food index, an internal fat index, a fat-digesting index, a carbohydrate-digesting index, a health index, a fasting index, a ketogenic index, . . . ). The microbiome data may also be utilized to determine microbiome ancestry for the user that indicates other users to which the user is related to.

Abstract: Techniques are disclosed herein for generating personalized food guidance using predicted food responses. Using the technologies described herein, instead of providing food guidance that is generalized for a group of individuals, personalized food guidance is provided that takes into account an individual's personalized responses to foods, such as the individual's glucose and fat responses, the individual's micro biome, the individual's overall health, potential health risks for the individual, and/or other data associated with the individual. Providing an individual with personalized food guidance can make choosing food easier and healthier.

Abstract: Techniques are disclosed herein for improving the accuracy of test data obtained outside of a clinical setting. Using the technologies described herein, different techniques can be utilized to analyze, score and adjust test data associated with one or more “at home” tests. In some examples, computing systems are utilized to generate quality scores indicating the accuracy of the test data associated with a particular biomarker. In other examples, an authorized user, such as a data manager can analyze the test data utilizing a user interface to generate scores and/or adjust the test data.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations for foods available from one or more food sources. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of personalized nutrition data, such as one or more target biomarkers, that are associated with an individual after eating the foods. Personalized nutritional recommendations for foods available from the food sources are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, triglycerides data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted personalized nutrition data. A nutrition service utilizes the predicted personalized nutrition data when generating the personalized nutritional recommendations.

Abstract: Techniques are disclosed herein for using standardized meals and digital devices can be utilized outside a clinical setting to assist in determining/predicting one or more clinical states associated with one or more individuals. Using the technologies described herein, different techniques can be utilized to for using nutritional response measurements. For example, response measurements can be obtained for two fat meals instead of a single fat meal. Data associated with a circadian rhythm (e.g., sleep times, awake times) can also be utilized to improve the accuracy of the measured biomarkers. In other examples, biome data and other data can be utilized.

Abstract: Techniques are disclosed herein for improving the accuracy of nutritional responses measured in a non-clinical setting. Using the technologies described herein, different techniques can be utilized to improve the accuracy of test data associated with one or more “at home” tests. In some examples, more than one test is utilized to improve the accuracy of test data associated with a particular biomarker. In other examples, a data accuracy service can programmatically analyze data received from an individual and determine whether the data is accurate. In some examples, a computing device is utilized to assist in determining what food item(s) are consumed, as well as determine whether a test protocol was followed.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.

Abstract: Digital data representing individual pixels of a video image frame are read and then encoded as a series of binary coded words describing blocks of pixels typically eight by eight for transmission or storage. When the words are decoded an assessment is made as to when a set of pixels representing a region of the video image frame signifying an object at least overlaps into other blocks. Subregions of the blocks in question which make up the whole region are identified and their pixel luminance and chrominance values and these values are interpolated across the region to smooth out transitions across boundaries artificially delimiting the subregions. A library of masks representing luminance values for all the pixels in a block can be made available in order to enhance the compression process.

Abstract: A method of producing lyocell fibers by spinning a solution of cellulose in an organic solvent through an air gap and into a spin bath in which there is provided a cross-draught of air in the air gap.

Abstract: A method of producing lyocell fibres by spinning a solution of cellulose in an organic solvent through an air gap and into a spin bath in which there is provided a cross-draught of air in the air gap.

Abstract: A spinning cell for producing lyocell fibre by spinning a solution of cellulose in an organic solvent through an air gap into a spin bath has nozzles to create a cross-draught through the air gap.