Abstract: Exemplary embodiments relate to improvements in robotic systems to reduce biological or chemical harborage points on the systems. For example, in exemplary embodiments, robotic actuators, hubs, or entire robotic systems may be configured to allow crevices along joints or near fasteners to be reduced or eliminated, hard corners to be replaced with rounded edges, certain components or harborage points to be eliminated, shapes to be reconfigured to be smoother or flat, and/or or surfaces to be reconfigurable for simpler cleaning.

Abstract: Systems and methods are disclosed to infer, using a machine learned model, a service protocol of a server based on the banner data produced by the server. In embodiments, the machine learned model is implemented by a network scanner configured to receive banner data from open ports on servers. A received banner is parsed into a set of features, such as the counts or presence of particular characters or strings in the banner. In embodiments, certain types of banner content such as network addresses, hostnames, dates, and times, are replaced with special characters. The machine learned model is applied to the features to infer a most likely protocol of the server port that produced the banner. Advantageously, the model can be trained to perform the inference task with high accuracy and without using human-specified rules, which can be brittle for unconventional banner data and carry undesired biases.

Abstract: Systems and methods are disclosed to infer, using a machine learned model, a service protocol of a server based on the banner data produced by the server. In embodiments, the machine learned model is implemented by a network scanner configured to receive banner data from open ports on servers. A received banner is parsed into a set of features, such as the counts or presence of particular characters or strings in the banner. In embodiments, certain types of banner content such as network addresses, hostnames, dates, and times, are replaced with special characters. The machine learned model is applied to the features to infer a most likely protocol of the server port that produced the banner. Advantageously, the model can be trained to perform the inference task with high accuracy and without using human-specified rules, which can be brittle for unconventional banner data and carry undesired biases.

Abstract: Exemplary embodiments relate to improvements in robotic systems to reduce biological or chemical harborage points on the systems. For example, in exemplary embodiments, robotic actuators, hubs, or entire robotic systems may be configured to allow crevices along joints or near fasteners to be reduced or eliminated, hard corners to be replaced with rounded edges, certain components or harborage points to be eliminated, shapes to be reconfigured to be smoother or flat, and/or or surfaces to be reconfigurable for simpler cleaning.

Abstract: Exemplary embodiments relate to improvements in robotic systems to reduce biological or chemical harborage points on the systems. For example, in exemplary embodiments, robotic actuators, hubs, or entire robotic systems may be configured to allow crevices along joints or near fasteners to be reduced or eliminated, hard corners to be replaced with rounded edges, certain components or harborage points to be eliminated, shapes to be reconfigured to be smoother or flat, and/or or surfaces to be reconfigurable for simpler cleaning.

Abstract: Breast density is a significant breast cancer risk factor measured from mammograms. Disclosed is a methodology for converting continuous measurements of breast density and calibrated mammograms into a four-state ordinal variable approximating the BI-RADS ratings. In particular, the present disclosure is directed to a calibration system for a specific full field digital mammography (FFDM) technology. The calibration adjusts for the x-ray acquisition technique differences across mammograms resulting in standardized images. The approach produced various calibrated and validated measures of breast density, one of which assesses variation in the mammogram referred to as Vc (i.e. variation measured from calibrated mammograms). The variation in raw mammograms [i.e. Vr] is a valid breast density risk factor in both FFDM in digitized film mammograms.

Abstract: In particular, disclosed is a method for treating a patient with prostate cancer that involves genotyping a nucleic acid sample from the subject for one or more single nucleotide polymorphism (SNP) alleles in one or more genes angiogenesis, comparing the one or more SNP alleles to control allele frequencies to produce a SNP signature, and analyzing the SNP signature to generate a risk score. The risk score can represent the likelihood that the patient's prostate cancer will recur following radical prostatectomy. In particular embodiments, a high risk score in a patient with positive margins is an indication of a high risk of prostate cancer recurrence.

Abstract: An automated percentage of breast density measure (PDa) that analyzes signal dependent noise (SDN) based on a wavelet expansion using full field digital mammography (FFDM). A matched case-control dataset is used with images acquired from a specific direct x-ray capture FFDM system. PDa is applied to the raw and clinical display representation images. Transforming (pixel mapping) of the raw image to another representation (raw-transformed) is performed using differential evolution optimization and investigated the influence of lowering the native spatial resolution of the image by a one-half. When controlling for body mass index, the quartile specific ORs for the associations of PDa with breast cancer varied with representation and resolution. PDa is a valid automated breast density measurement for a specific FFDM technology and compares well against PD (operator-assisted or the standard) when applied to either the raw-transformed or clinical display images from this FFDM technology.

Abstract: Breast density is a significant breast cancer risk factor measured from mammograms. Disclosed is a methodology for converting continuous measurements of breast density and calibrated mammograms into a four-state ordinal variable approximating the BI-RADS ratings. In particular, the present disclosure is directed to a calibration system for a specific full field digital mammography (FFDM) technology. The calibration adjusts for the x-ray acquisition technique differences across mammograms resulting in standardized images. The approach produced various calibrated and validated measures of breast density, one of which assesses variation in the mammogram referred to as Vc (i.e. variation measured from calibrated mammograms). The variation in raw mammograms [i.e. Vr] is a valid breast density risk factor in both FFDM in digitized film mammograms.

Abstract: Exemplary embodiments relate to improvements in robotic systems to reduce biological or chemical harborage points on the systems. For example, in exemplary embodiments, robotic actuators, hubs, or entire robotic systems may be configured to allow crevices along joints or near fasteners to be reduced or eliminated, hard corners to be replaced with rounded edges, certain components or harborage points to be eliminated, shapes to be reconfigured to be smoother or flat, and/or or surfaces to be reconfigurable for simpler cleaning.

Abstract: Breast density is a significant breast cancer risk factor measured from mammograms. Disclosed is a methodology for converting continuous measurements of breast density and calibrated mammograms into a four-state ordinal variable approximating the BI-RADS ratings. In particular, the present disclosure is directed to a calibration system for a specific full field digital mammography (FFDM) technology. The calibration adjusts for the x-ray acquisition technique differences across mammograms resulting in standardized images. The approach produced various calibrated and validated measures of breast density, one of which assesses variation in the mammogram referred to as Vc (i.e. variation measured from calibrated mammograms). The variation in raw mammograms [i.e. Vr] is a valid breast density risk factor in both FFDM in digitized film mammograms.

Abstract: An automated percentage of breast density measure (PDa) that analyzes signal dependent noise (SDN) based on a wavelet expansion using full field digital mammography (FFDM). A matched case-control dataset is used with images acquired from a specific direct x-ray capture FFDM system. PDa is applied to the raw and clinical display representation images. Transforming (pixel mapping) of the raw image to another representation (raw-transformed) is performed using differential evolution optimization and investigated the influence of lowering the native spatial resolution of the image by a one-half When controlling for body mass index, the quartile specific ORs for the associations of PDa with breast cancer varied with representation and resolution. PDa is a valid automated breast density measurement for a specific FFDM technology and compares well against PD (operator-assisted or the standard) when applied to either the raw-transformed or clinical display images from this FFDM technology.

Abstract: Breast density is a significant breast cancer risk factor measured from mammograms. Disclosed is a methodology for converting continuous measurements of breast density and calibrated mammograms into a four-state ordinal variable approximating the BI-RADS ratings. In particular, the present disclosure is directed to a calibration system for a specific full field digital mammography (FFDM) technology. The calibration adjusts for the x-ray acquisition technique differences across mammograms resulting in standardized images. The approach produced various calibrated and validated measures of breast density, one of which assesses variation in the mammogram referred to as Vc (i.e. variation measured from calibrated mammograms). The variation in raw mammograms [i.e. Vr] is a valid breast density risk factor in both FFDM in digitized film mammograms.

Abstract: Exemplary embodiments relate to improvements in robotic systems to reduce biological or chemical harborage points on the systems. For example, in exemplary embodiments, robotic actuators, hubs, or entire robotic systems may be configured to allow crevices along joints or near fasteners to be reduced or eliminated, hard corners to be replaced with rounded edges, certain components or harborage points to be eliminated, shapes to be reconfigured to be smoother or flat, and/or or surfaces to be reconfigurable for simpler cleaning.

Abstract: An automated percentage of breast density measure (PDa) that analyzes signal dependent noise (SDN) based on a wavelet expansion using full field digital mammography (FFDM). A matched case-control dataset is used with images acquired from a specific direct x-ray capture FFDM system. PDa is applied to the raw and clinical display representation images. Transforming (pixel mapping) of the raw image to another representation (raw-transformed) is performed using differential evolution optimization and investigated the influence of lowering the native spatial resolution of the image by a one-half. When controlling for body mass index, the quartile specific ORs for the associations of PDa with breast cancer varied with representation and resolution. PDa is a valid automated breast density measurement for a specific FFDM technology and compares well against PD (operator-assisted or the standard) when applied to either the raw-transformed or clinical display images from this FFDM technology.

Abstract: In particular, disclosed is a method for treating a patient with prostate cancer that involves genotyping a nucleic acid sample from the subject for one or more single nucleotide polymorphism (SNP) alleles in one or more genes angiogenesis, comparing the one or more SNP alleles to control allele frequencies to produce a SNP signature, and analyzing the SNP signature to generate a risk score. The risk score can represent the likelihood that the patient's prostate cancer will recur following radical prostatectomy. In particular embodiments, a high risk score in a patient with positive margins is an indication of a high risk of prostate cancer recurrence.

Abstract: Exemplary embodiments relate to improvements in robotic systems to reduce biological or chemical harborage points on the systems. For example, in exemplary embodiments, robotic actuators, hubs, or entire robotic systems may be configured to allow crevices along joints or near fasteners to be reduced or eliminated, hard corners to be replaced with rounded edges, certain components or harborage points to be eliminated, shapes to be reconfigured to be smoother or flat, and/or or surfaces to be reconfigurable for simpler cleaning.

Abstract: Breast density is a significant breast cancer risk factor measured from mammograms. Disclosed is a methodology for converting continuous measurements of breast density and calibrated mammograms into a four-state ordinal variable approximating the BI-RADS ratings. In particular, the present disclosure is directed to a calibration system for a specific full field digital mammography (FFDM) technology. The calibration adjusts for the x-ray acquisition technique differences across mammograms resulting in standardized images. The approach produced various calibrated and validated measures of breast density, one of which assesses variation in the mammogram referred to as Vc (i.e. variation measured from calibrated mammograms). The variation in raw mammograms [i.e. Vr] is a valid breast density risk factor in both FFDM in digitized film mammograms.

Abstract: An automated percentage of breast density measure (PDa) that analyzes signal dependent noise (SDN) based on a wavelet expansion using full field digital mammography (FFDM). A matched case-control dataset is used with images acquired from a specific direct x-ray capture FFDM system. PDa is applied to the raw and clinical display representation images. Transforming (pixel mapping) of the raw image to another representation (raw-transformed) is performed using differential evolution optimization and investigated the influence of lowering the native spatial resolution of the image by a one-half. When controlling for body mass index, the quartile specific ORs for the associations of PDa with breast cancer varied with representation and resolution. PDa is a valid automated breast density measurement for a specific FFDM technology and compares well against PD (operator-assisted or the standard) when applied to either the raw-transformed or clinical display images from this FFDM technology.

Abstract: Breast density is a significant breast cancer risk factor measured from mammograms. Evidence suggests that the spatial variation in mammograms may also be associated with risk. The variation in calibrated mammograms as a breast cancer risk factor was investigated and its relationship with other measures of breast density was explored using full field digital mammography (FFDM) as described herein. A matched case-control analysis was used to assess a spatial variation breast density measure in calibrated FFDM images, normalized for the image acquisition technique variation. The findings indicate the variation measure is a viable automated method for assessing breast density. Insights gained by this work may be used to develop a standard for measuring breast density.