1. A method of detecting a subject suffering from, or at risk of suffering from, bladder cancer, the method comprising
i) providing a body fluid sample isolated from a subject;
ii) isolating cells from said sample to provide a cell sample;
iii) contacting the sample with a specific binding member capable of binding to a minichromosome maintenance (MCM) polypeptide(s);
iv) determining the binding of said specific binding member to the cell sample;
v) counting those cells in said cell sample which bound to said specific binding member to provide a cell count;
vi) determining, based on the cell count, whether the subject has, or is at risk of having, bladder cancer.
2. The method of claim 1, wherein said determining step (vi) is based upon a measurement of MCM labelled cells relative to a threshold number, wherein said measurement above or equal to said threshold is indicative of bladder cancer or a risk of bladder cancer.
3. The method of claim 1, wherein the subject presents with symptoms associated with bladder cancer.
4. The method of claim 3 wherein the symptoms of bladder cancer include haematuria.
5. The method of claim 1, wherein the threshold number is at least about 10 cells.
6. The method of claim 5 wherein the threshold number is at least about 30 cells.
7. The method of claim 6 wherein the threshold number is at least about 50 cells.
8. The method of claim 7 wherein the threshold number is between about 50 and 400 cells.
9. The method of claim 1, wherein the subject has a previous occurrence of bladder cancer or has relapsed.
10. The method of claim 1, wherein the threshold number is at least about 10 cells.
11. The method of claim 10 wherein the threshold number is at least about 30 cells.
12. The method of claim 11, wherein the threshold number is at least about 50 cells.
13. The method of claim 12, wherein the threshold number is at least about 200 cells.
14. The method of claim 13, wherein the threshold number is between about 200 and 400 cells.
15. A method of detecting a subject suffering from, or at risk of suffering from, bladder cancer, the method comprising
i) providing a body fluid sample isolated from a subject;
ii) isolating cells from said sample to provide a cell sample;
iii) contacting the sample with a specific binding member capable of binding to a minichromosome maintenance (MCM) polypeptide(s);
iv) determining the binding of said specific binding member to the cell sample;
v) counting those cells in said cell sample which bound to said specific binding member to provide a cell count;
vi) determining, based on the cell count, whether the subject has, or is at risk of having, bladder cancer
wherein said determining step (vi) is based upon a measurement of MCM bound cells relative to a threshold number, wherein said measurement above or equal to said threshold is indicative of bladder cancer or a risk of bladder cancer.
16. The method of claim 8, wherein the threshold number is at least about 50 cells.
17. The method of claim 9, wherein the threshold number is between about 50 and 400 cells.
18. The method of claim 10, wherein the threshold number is about 50 cells.
19. A method of detecting bladder cancer in a subject who has a previous occurrence of bladder cancer or has relapsed, the method comprising
i) providing a body fluid sample isolated from a subject;
ii) isolating cells from said sample to provide a cell sample;
iii) contacting the sample with a specific binding member capable of binding to a minichromosome maintenance (MCM) polypeptide(s);
iv) determining the binding of said specific binding member to the cell sample;
v) counting those cells in said cell sample which bound to said specific binding member to provide a cell count;
vi) determining, based on the cell count, whether the subject has, or is at risk of having, a recurrence of bladder cancer
wherein said determining step (vi) is based upon a measurement of MCM bound cells relative to a threshold number, wherein said measurement above or equal to said threshold is indicative of bladder cancer or a risk of recurrence of bladder cancer.
20. The method of claim 19, wherein the threshold number is at least about 200 cells.
21. The method of claim 9, wherein the threshold number is between about 200 and 400 cells.
22. The method of claim 10, wherein the threshold number is about 200 cells.
23. The method of claim 19, wherein the body fluid is urine.
24. The method of claim 19, wherein the MCM is selected from the group consisting of MCM 2, 3, 4, 5, 6 and 7.
25. The method of claim 24, wherein the MCM is selected from the group consisting of MCM 2, 5 and 7.
26. The method of claim 25, wherein the MCM is selected from the group consisting of MCM 2 and 7.
27. The method of claim 26, wherein the MCM is MCM 2.
28. The method of claim 19, wherein the MCM is MCM 7.
29. The method of claim 19, wherein the cells are isolated from the body fluid sample by filtration of the body fluid sample.
30. The method of claim 19, wherein the specific binding member is an antibody, or fragment thereof.
31. The method of claim 30, wherein the antibody is a monoclonal antibody.
32. The method of claim 19, wherein the cell count is determined by flow cytometry.
33. The method of claim 19, wherein the cell count is determined using an automated cell counter.
34. A method to diagnose and treat a subject suffering, or suspected from suffering, from bladder cancer comprising the steps:
i) providing a fluid sample isolated from a subject;
ii) isolating cells from said sample to provide a cell sample;
iii) contacting the sample with a specific binding member capable of binding to a mini chromosome maintenance (MCM) polypeptide(s);
iv) determining the binding of said specific binding member to the cell sample;
v) counting those cells in said cell sample which bound to said specific binding member to provide a cell count;
vi) determining, based on the cell count, whether the subject has, or is at risk of having, bladder cancer;
vii) determining a treatment regime to prevent andor treat the subject’s suspected bladder cancercancer respectively; and
viii) administering said treatment regime to prevent andor treat the subject’s suspected bladder cancercancer respectively.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method of analyzing network traffic comprising:
selecting a primary metric from a plurality of collected metrics associated with network element metrics andor node-to-node contexts;
generating correlation coefficients between the primary metric and ones of at least a portion of the plurality of collected metrics; and
generating a hypothesis set based on the correlation coefficients,
wherein at least one of selecting a primary metric, generating correlation coefficients, and generating a hypothesis set is performed using at least one processor.
2. The method of analyzing network traffic of claim 1, further comprising identifying, based on a defined setting, a candidate set corresponding to the portion of the plurality of collected metrics.
3. The method of analyzing network traffic of claim 2, wherein identifying the candidate set comprises selecting a scope of network elements to include in the candidate set.
4. The method of analyzing network traffic of claim 3, wherein the scope includes selecting network elements within N network hops, wherein N>=1.
5. The method of analyzing network traffic of claim 3, wherein the scope includes selecting all network elements that are associated with the selected primary metric.
6. The method of analyzing network traffic of claim 2, wherein identifying the candidate set comprises selecting a network metric type that identifies a type of network metrics to include in the candidate set.
7. The method of analyzing network traffic of claim 6, wherein identifying the candidate set further comprises:
applying a filtering function to the plurality of collected metrics associated with network element metrics andor node-to-node contexts.
8. The method of analyzing network traffic of claim 7, wherein applying the filter function further comprises:
receiving an input associated with the network metric type; and
identifying members of the candidate set from the plurality of collected metrics based on the received input.
9. The method of analyzing network traffic of claim 8, wherein receiving the input associated with the network metric type comprises:
receiving input from a user via a user input interface; and
generating the network metric type based on the input from the user.
10. The method of analyzing network traffic of claim 8, wherein receiving the input associated with the network metric type comprises:
reading a data file that includes configuration information; and
generating the network metric type based on the configuration information.
11. The method of analyzing network traffic of claim 6, wherein selecting the network metric type comprises selecting transactions as the network metric type to include in the candidate set.
12. The method of analyzing network traffic of claim 6, wherein selecting the network metric type comprises selecting virtual machines as the network metric type to include in the candidate set.
13. The method of analyzing network traffic of claim 6, wherein selecting the network metric type comprises selecting network infrastructure metrics as the network metric type to include in the candidate set.
14. The method of analyzing network traffic of claim 6, wherein selecting the network metric type comprises selecting links between nodes in the network as the network metric type to include in the candidate set.
15. The method of analyzing network traffic of claim 6, wherein selecting the network metric type comprises selecting processes running on nodes in the network as the network metric type to include in the candidate set.
16. The method of analyzing network traffic of claim 6, wherein selecting the network metric type comprises selecting server stacks as the network metric type to include in the candidate set.
17. The method of analyzing network traffic of claim 2, further comprising:
displaying, on a display, information that represents the primary metric; and
displaying in tabular form, on a display, for one or more members of the candidate set, respective information associated with a metric source, context type, network element identification, andor corresponding correlation coefficient.
18. The method of analyzing network traffic of claim 1, wherein the correlation coefficient is calculated using Pearson’s correlation coefficient.
19. The method of analyzing network traffic of claim 1, wherein generating the hypothesis set comprises:
identifying the primary metric and the respective collected metric for which the respective correlation coefficient is greater than a first threshold or less than a second threshold; and
adding to the hypothesis set, the identified primary metric and the respective identified collected metric,
wherein the correlation coefficient being greater than the first threshold corresponds to a positive correlation coefficient and the correlation coefficient being less than the second threshold corresponds to a negative correlation coefficient.
20. The method of analyzing network traffic of claim 1, further comprising:
displaying, on a display, information representing the primary metric, at least one of the portion of the plurality of collected metrics, and the correlation coefficients corresponding to the at least one of the portion of the plurality of collected metrics.
21. The method of analyzing network traffic of claim 1, further comprising:
displaying, on a display, data graphs representative of the primary metric and at least one of the portion of the plurality of collected metrics.
22. The method of analyzing network traffic of claim 1, further comprising:
displaying, on a display, information associated with one or more members of the hypothesis set in a visually distinctive manner relative to other ones of the portion of the plurality of collected metrics.
23. The method of analyzing network traffic of claim 1, further comprising:
generating heat maps based on the correlation coefficients, wherein color-coded variations are used to distinguish varying levels of correlation between the between the primary metric and ones of at least a portion of the plurality of collected metrics.
24. The method of analyzing network traffic of claim 1,
wherein the portion of the plurality of collected metrics includes all of the plurality of the collected metrics except the primary metric, and
wherein generating the correlation coefficients comprises generating the correlation coefficients between the primary metric and each of the portion of the plurality of the collected metrics.
25. The method of analyzing network traffic of claim 1,
wherein data corresponding to the primary metric corresponds to a first time and data corresponding to the ones of at least the portion of the plurality of the collected metrics corresponds to a second time that is different from the first time by a time interval.
wherein the generating a hypothesis set comprises generating a temporally shifted hypothesis set.
26. The method of analyzing network traffic of claim 25, wherein the time interval is a multiple of a data collection interval corresponding to the collected metrics.
27. The method of analyzing network traffic of claim 25, further comprising:
receiving an increment andor decrement input via a user interface; and
adjusting the time interval based on the received increment andor decrement input.
28. The method of analyzing network traffic of claim 25, further comprising:
receiving a temporal shift value from a user interface, wherein the time interval is adjusted based on the received temporal shift value.
29. A computer program product, the computer program product comprising a computer usable storage medium having computer-readable program code embodied in the medium, the computer readable program code configured to perform the method of claim 1.
30. A method of analyzing network traffic comprising:
generating correlation coefficients between ones of a plurality of collected metrics associated with network element metrics andor node-to-node contexts and all other ones of the plurality of collected metrics; and
generating a hypothesis set based on the correlation coefficients,
wherein at least one of generating the correlation coefficients and generating a hypothesis set is performed using at least one processor.
31. The method of analyzing network traffic of claim 30,
wherein data corresponding to the ones of a plurality of collected metrics corresponds to a first time and data corresponding to the all other ones of the plurality of the collected metrics corresponds to a second time that is different from the first time by a time interval, and
wherein the generating a hypothesis set comprises generating a temporally shifted hypothesis set.
32. The method of analyzing network traffic of claim 31, wherein the time interval is a multiple of a data collection interval corresponding to the collected metrics.
33. The method of analyzing network traffic of claim 31, further comprising:
receiving an increment andor decrement input via a user interface; and
adjusting the time interval based on the received increment andor decrement input.
34. The method of analyzing network traffic of claim 31, further comprising:
receiving a temporal shift value from a user interface,
wherein the time interval is adjusted based on the received temporal shift value.