Abstract: In an embodiment, a method includes applying a liquid plural component polymer to a sloped roof to form a protective surface that inhibits moisture seepage to the roof sheathing. In some embodiments, the plural component polymer may be a polyurea compound having a hardening time that is less than approximately 10 minutes. In some embodiments, one or more of the components may be aerated prior to application to the roof substrate. In some embodiments, one or more polystyrene panels may be affixed to the sloped roof prior to application of the liquid plural component polymer. Other embodiments are described and claimed.

Abstract: The ability to automate the processes of specimen collection, image acquisition, data pre-processing, computation of derived biomarkers, modeling, classification and analysis can significantly impact clinical decision-making and fundamental investigation of cell deformation. This disclosure combine 3D cell nuclear shape modeling by robust smooth surface reconstruction and extraction of shape morphometry measure into a highly parallel pipeline workflow protocol for end-to-end morphological analysis of thousands of nuclei and nucleoli in 3D. This approach allows efficient and informative evaluation of cell shapes in the imaging data and represents a reproducible technique that can be validated, modified, and repurposed by the biomedical community. This facilitates result reproducibility, collaborative method validation, and broad knowledge dissemination.

Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.

Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.

Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.

Abstract: To analyze spatial organization of chromatin a computing device may compile genomic element contacts or reads into variable size bins using a binary search tree. The bins may be selected to each represent a different cutsite increment or functional element within a genome, such as a gene, TAD, chromatin state segment, loop domain, chromatin domain, etc. Two sets of bins are selected to generate a squared genome matrix of bin pairs, where each set represent an axis of the matrix. Then a normalization method is applied to the interaction frequencies for the bin pairs having variable size and/or shape to generate normalized interaction frequencies for each bin pair. The normalized interaction frequencies may be used to identify bin pairs having enriched and depleted contacts for a variety of analyses, including the detection of target genes of genomic variants, as well as genome wide analysis of contacts.

Abstract: The ability to automate the processes of specimen collection, image acquisition, data pre-processing, computation of derived biomarkers, modeling, classification and analysis can significantly impact clinical decision-making and fundamental investigation of cell deformation. This disclosure combine 3D cell nuclear shape modeling by robust smooth surface reconstruction and extraction of shape morphometry measure into a highly parallel pipeline workflow protocol for end-to-end morphological analysis of thousands of nuclei and nucleoli in 3D. This approach allows efficient and informative evaluation of cell shapes in the imaging data and represents a reproducible technique that can be validated, modified, and repurposed by the biomedical community. This facilitates result reproducibility, collaborative method validation, and broad knowledge dissemination.

Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.

Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic, physiomic, environmental, sociomic, demographic, and outcome phenotype data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc.

Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic, physiomic, environmental, sociomic, demographic, and outcome phenotype data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc.

Abstract: In an embodiment, a method includes applying a liquid plural component polymer to a sloped roof to form a protective surface that inhibits moisture seepage to the roof sheathing. In some embodiments, the plural component polymer may be a polyurea compound having a hardening time that is less than approximately 10 minutes. In some embodiments, one or more of the components may be aerated prior to application to the roof substrate. In some embodiments, one or more polystyrene panels may be affixed to the sloped roof prior to application of the liquid plural component polymer. Other embodiments are described and claimed.

Abstract: In an embodiment, a method includes applying a liquid plural component polymer to a sloped roof to form a protective surface that inhibits moisture seepage to the roof sheathing. In some embodiments, the plural component polymer may be a polyurea compound having a hardening time that is less than approximately 10 minutes. In some embodiments, one or more of the components may be aerated prior to application to the roof substrate. In some embodiments, one or more polystyrene panels may be affixed to the sloped roof prior to application of the liquid plural component polymer. Other embodiments are described and claimed.

Abstract: In an embodiment, a method includes applying a liquid plural component polymer to a sloped roof to form a protective surface that inhibits moisture seepage to the roof sheathing. In some embodiments, the plural component polymer may be a polyurea compound having a hardening time that is less than approximately 10 minutes. In some embodiments, one or more of the components may be aerated prior to application to the roof substrate. In some embodiments, one or more polystyrene panels may be affixed to the sloped roof prior to application of the liquid plural component polymer. Other embodiments are described and claimed.