Abstract: A method is disclosed of predicting cancer patient response to immune checkpoint inhibitors, e.g., an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) or CTLA4. The method includes obtaining mass spectrometry data from a blood-based sample of the patient, obtaining integrated intensity values in the mass spectrometry data of a multitude of pre-determined mass-spectral features; and operating on the mass spectral data with a programmed computer implementing a classifier. The classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of melanoma patients with a classification algorithm and generates a class label for the sample. A class label “early” or the equivalent predicts the patient is likely to obtain relatively less benefit from the antibody drug and the class label “late” or the equivalent indicates the patient is likely to obtain relatively greater benefit from the antibody drug.

Abstract: Methods and means for evaluating and summarizing physical quality metrics associated with stretch films and other plastics are provided. According to one aspect of the invention, a processor- or computer-enabled business method such as a software program or mobile application capable of calculating and comparing stretch film data and conveying easily understood summaries of film characteristics and cost impacts associated therewith is provided. According to a second aspect, the system and method provides a vast plurality of film performance metrics, costs factor, environmental impacts, and so, calculated and presented in a dashboard-type user interface.

Abstract: Laboratory test apparatus for conducting a mass spectrometry test on a blood-based sample of a cancer patient includes a classification procedure implemented in a programmed computer that generates a class label. In one form of the test, “Test 1”, if the sample is labelled “Bad” or equivalent the patient is predicted to exhibit primary immune resistance if they are later treated with anti-PD-1 or anti-PD-L1 therapies. In “Test 2” the Bad class label predicts that the patient will have a poor prognosis in response to treatment by either anti-PD-1 or anti-PD-L1 therapies or alternative chemotherapies, such as docetaxel or pemetrexed. “Test 3” identifies patients that are likely to have a poor prognosis in response to treatment by either anti-PD-1 or anti-PD-L1 therapies but have improved outcomes on alternative chemotherapies. A Good class label by either Test 1 or 2 predicts very good outcome on anti-PD-1 or anti-PD-L1 monotherapy.

Abstract: Using a traffic monitoring service to detect traffic pattern shifts can include obtaining netflow data and routing data for core routers. Based on the netflow data and the routing data, an augmented traffic matrix can be generated. A router-level analysis can be performed on the augmented traffic matrix to identify a pair of core routers that are associated with a traffic pattern shift in the network. An entity-level analysis can be performed on the pair of core routers to identify an entity that is responsible for the traffic pattern shift in the network. Traffic shift data that identifies the pair of core routers and the entity that is responsible for the traffic pattern shift in the network can be output.

Abstract: A method is disclosed for predicting in advance whether a melanoma patient is likely to benefit from high dose IL2 therapy in treatment of the cancer. The method makes use of mass spectrometry data obtained from a blood-based sample of the patient and a computer configured as a classifier and making use of a reference set of mass spectral data obtained from a development set of blood-based samples from other melanoma patients. A variety of classifiers for making this prediction are disclosed, including a classifier developed from a set of blood-based samples obtained from melanoma patients treated with high dose IL2 as well as melanoma patients treated with an anti-PD-1 immunotherapy drug. The classifiers developed from anti-PD-1 and IL2 patient sample cohorts can also be used in combination to guide treatment of a melanoma patient.

Abstract: Methods and means for evaluating and summarizing physical quality metrics associated with stretch films and other plastics are provided. According to one aspect of the invention, a processor- or computer-enabled business method such as a software program or mobile application capable of calculating and comparing stretch film data and conveying easily understood summaries of film characteristics and cost impacts associated therewith is provided. According to a second aspect, the system and method provides a vast plurality of film performance metrics, costs factor, environmental impacts, and so, calculated and presented in a dashboard-type user interface.

Abstract: This invention provides an antibody or functional antibody fragments, or probe thereof directed against a unique group of antigens identified in cancer. The present invention comprises nucleotide sequences derived from L2A5 monoclonal antibody. The antibody or functional antibody fragment, or probe thereof includes a variable heavy chain domain and a variable light chain domain that has an amino acid sequence provided herein. This DNA/amino acid sequence conjugation is unique and has never been described before. The present invention further provides antibody or functional antibody fragment or a conjugate or a recombinant protein useful in the detection, treatment and prevention of human disease, including cancer.

Abstract: A blood-based sample from a cancer patient is subject to mass spectrometry and the resulting mass spectral data is classified with the aid of a computer to see if the patient is a member of a class of patients having a poor prognosis. If so, the mass spectral data is further classified with the aid of the computer by a second classifier which identifies whether the patient is nevertheless likely to obtain durable benefit from immunotherapy drugs, e.g., immune checkpoint inhibitors, anti-CTLA4 drugs, and high dose interleukin-2.

Abstract: A blood-based sample from a cancer patient is subject to mass spectrometry and the resulting mass spectral data is classified with the aid of a computer to see if the patient is a member of a class of patients having a poor prognosis. If so, the mass spectral data is further classified with the aid of the computer by a second classifier which identifies whether the patient is nevertheless likely to obtain durable benefit from immunotherapy drugs, e.g., immune checkpoint inhibitors, anti-CTLA4 drugs, and high dose interleukin-2.

Abstract: A laboratory test apparatus for conducting a mass spectrometry test on a blood-based sample of a cancer patient includes a classification procedure implemented in a programmed computer that generates a class label for the sample. In one form of the test, “Test 1” herein, if the sample is labelled “Bad” or equivalent the patient is predicted to exhibit primary immune resistance if they are later treated with anti-PD-1 or anti-PD-L1 therapies in treatment of the cancer. In another configuration of the test, “Test 2” herein, the Bad class label predicts that the patient will have a poor prognosis in response to treatment by either anti-PD-1 or anti-PD-L1 therapies or alternative chemotherapies, such as docetaxel or pemetrexed. “Test 3” identifies patients that are likely to have a poor prognosis in response to treatment by either anti-PD-1 or anti-PD-L1 therapies but have improved outcomes on alternative chemotherapies.

Abstract: A method is disclosed of predicting cancer patient response to immune checkpoint inhibitors, e.g., an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) or CTLA4. The method includes obtaining mass spectrometry data from a blood-based sample of the patient, obtaining integrated intensity values in the mass spectrometry data of a multitude of pre-determined mass-spectral features; and operating on the mass spectral data with a programmed computer implementing a classifier. The classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of melanoma patients with a classification algorithm and generates a class label for the sample. A class label “early” or the equivalent predicts the patient is likely to obtain relatively less benefit from the antibody drug and the class label “late” or the equivalent indicates the patient is likely to obtain relatively greater benefit from the antibody drug.

Abstract: A method is disclosed of predicting cancer patient response to immune checkpoint inhibitors, e.g., an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) or CTLA4. The method includes obtaining mass spectrometry data from a blood-based sample of the patient, obtaining integrated intensity values in the mass spectrometry data of a multitude of pre-determined mass-spectral features; and operating on the mass spectral data with a programmed computer implementing a classifier. The classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of melanoma patients with a classification algorithm and generates a class label for the sample. A class label “early” or the equivalent predicts the patient is likely to obtain relatively less benefit from the antibody drug and the class label “late” or the equivalent indicates the patient is likely to obtain relatively greater benefit from the antibody drug.

Abstract: This invention provides an antibody or functional antibody fragments, or probe thereof directed against a unique group of antigens identified in cancer. The present invention comprises nucleotide sequences derived from L2A5 monoclonal antibody. The antibody or functional antibody fragment, or probe thereof includes a variable heavy chain domain and a variable light chain domain that has an amino acid sequence provided herein. This DNA/amino acid sequence conjugation is unique and has never been described before. The present invention further provides antibody or functional antibody fragment or a conjugate or a recombinant protein useful in the detection, treatment and prevention of human disease, including cancer.

Abstract: Classifier generation methods are described in which features used in classification (e.g., mass spectral peaks) are selected, or deselected using bagged filtering. A development sample set is split into two subsets, one of which is used as a training set the other of which is set aside. We define a classifier (e.g., K-nearest neighbor, decision tree, margin-based classifier or other) using the training subset and at least one of the features (or subsets of two or more features in combination). We apply the classifier to a subset of samples. A filter is applied to the performance of the classifier on the sample subset and the at least one feature is added to a “filtered feature list” if the classifier performance passes the filter. We do this for many different realizations of the separation of the development sample set into two subsets, and, for each realization, different features or sets of features in combination.

Abstract: The present invention relates to an anti-erosion system made of geo-synthetic material, preferably of polysteel raw material (polypropylene (PP) and polyethylene (PE) mixture) instead of only polypropylene or polyester or nylon, resulting in a compromise between the mechanical properties of polypropylene, the chemical properties of polyethylene, and the good resistance to the environmental agents achieved through the incorporation of molecular chain stabilizers. The fraction of PP should range from 50% to 90% and the fraction of PE from 10% to 50%.

Abstract: A programmed computer functioning as a classifier operates on mass spectral data obtained from a blood-based patient sample to predict indolence or aggressiveness of prostate cancer. Methods of generating the classifier and conducting a test on a blood-based sample from a prostate cancer patient using the classifier are described.

Abstract: A blood-based sample from a cancer patient is subject to mass spectrometry and the resulting mass spectral data is classified with the aid of a computer to see if the patient is a member of a class of patients having a poor prognosis. If so, the mass spectral data is further classified with the aid of the computer by a second classifier which identifies whether the patient is nevertheless likely to obtain durable benefit from immunotherapy drugs, e.g., immune checkpoint inhibitors, anti-CTLA4 drugs, and high dose interleukin-2.

Abstract: The present invention relates to an anti-erosion system made of geo-synthetic material, preferably of polysteel raw material (polypropylene (PP) and polyethylene (PE) mixture) instead of only polypropylene or polyester or nylon, resulting in a compromise between the mechanical properties of polypropylene, the chemical properties of polyethylene, and the good resistance to the environmental agents achieved through the incorporation of molecular chain stabilizers. The fraction of PP should range from 50% to 90% and the fraction of PE from 10% to 50%.

Abstract: Hepatocellular carcinoma (HCC) is detected in a patient with liver disease. Mass spectrometry data from a blood-based sample from the patient is compared to a reference set of mass-spectrometry data from a multitude of other patients with liver disease, including patients with and without HCC, in a general purpose computer configured as a classifier. The classifier generates a class label, such as HCC or No HCC, for the test sample. A laboratory system for early detection of HCC in patients with liver disease is also disclosed. Alternative testing strategies using AFP measurement and a reference set for classification in the form of class-labeled mass spectral data from blood-based samples of lung cancer patients are also described, including multi-stage testing.

Abstract: A method is disclosed for predicting in advance whether a melanoma patient is likely to benefit from high dose IL2 therapy in treatment of the cancer. The method makes use of mass spectrometry data obtained from a blood-based sample of the patient and a computer configured as a classifier and making use of a reference set of mass spectral data obtained from a development set of blood-based samples from other melanoma patients. A variety of classifiers for making this prediction are disclosed, including a classifier developed from a set of blood-based samples obtained from melanoma patients treated with high dose IL2 as well as melanoma patients treated with an anti-PD-1 immunotherapy drug. The classifiers developed from anti-PD-1 and IL2 patient sample cohorts can also be used in combination to guide treatment of a melanoma patient.