Author: claimsbot

1. A transistor device comprising:
a gate region formed on a substrate;
a well region formed within the substrate; and
a drain region comprising at least one retrograde well placed in the substrate next to the gate region,
wherein the retrograde well reduces a dopant concentration near the surface of the substrate, and the retrograde well and the well region substantially have the same dopant distribution profile.
2. The transistor device of claim 1 wherein the gate region further comprises one or more spacers.
3. The transistor device of claim 1 further comprising a doped region formed in the drain region.
4. The transistor device of claim 3 wherein at least a portion of the doped region is formed within the retrograde well.
5. The transistor device of claim 4 further comprising an isolation structure placed between the doped region and the gate region.
6. The transistor device of claim 1 further comprising a deep well, in which the drain region is formed.
7. The transistor device of claim 6 wherein the drain region is a well shallower than the deep well.
8. A transistor device comprising:
a substrate;
a gate region formed on the substrate;
a deep well formed in the substrate;
a well region formed within the substrate;
a source region formed directly in the deep well on a first side of the gate region;
one or more retrograde wells placed in the substrate next to the gate region and on a second side thereof;
the one or more retrograde wells forming a doped drain region, and
wherein the one or more retrograde wells further form a drain extension surrounding the doped drain region to have a lower dopant concentration toward the surface of the substrate, and the one or more retrograde wells and the well region substantially have the same dopant distribution profile.
9. The transistor device of claim 8 wherein the gate region further comprises one or more spacers.
10. The transistor device of claim 8 further comprising at least one isolation structure between the doped drain region and the gate region.
11. The transistor device of claim 8 wherein the deep well and the retrograde wells have different types of dopant.
12. The transistor device of claim 8 wherein the retrograde wells have a shallower doping profile than the deep well.
13. A transistor device comprising:
a substrate including a surface;
a gate region formed on the substrate;
a deep well formed in the substrate;
a well region formed within the substrate;
a source region formed directly in the deep well on a first side of the gate region; and
a doped drain region formed of a plurality of retrograde wells formed in the substrate next to the gate region and on a second opposed side of the gate region, at least one of the plurality of retrograde wells forming a drain extension surrounding the doped drain region and the retrograde wells having a lower dopant concentration toward the surface of the substrate, wherein the retrograde wells and the well region substantially have the same dopant distribution profile.

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. An isolated nucleic acid encoding the humanized antibody MLN02, comprising a first nucleotide sequence and a second nucleotide sequence, wherein
said first nucleotide sequence encodes the humanized light chain, comprises nucleotides 58-714 of SEQ ID NO:9 and encodes amino acids 20-238 of SEQ ID NO:11; and
said second nucleotide sequence encodes the humanized heavy chain, comprises nucleotides 58-1410 of SEQ ID NO:10 and encodes amino acids 20-470 of SEQ ID NO:12.
2. The isolated nucleic acid of claim 1, wherein said first nucleotide sequence comprises nucleotides 79-735 of SEQ ID NO:2 or nucleotides 78-734 of SEQ ID NO:4.
3. The isolated nucleic acid of claim 1, wherein said second nucleotide sequence comprises nucleotides 77-1429 of SEQ ID NO:1 or nucleotides 76-1428 of SEQ ID NO:3.
4. An isolated nucleic acid encoding the humanized immunoglobulin light chain of the humanized antibody MLN02, wherein said isolated nucleic acid comprises nucleotides 58-714 of SEQ ID NO:9 and encodes amino acids 20-238 of SEQ ID NO:11.
5. The isolated nucleic acid of claim 4, wherein said isolated nucleic acid comprises nucleotides 78-734 of SEQ ID NO:4.
6. The isolated nucleic acid of claim 4, wherein said isolated nucleic acid comprises nucleotides 79-735 of SEQ ID NO:2.
7. An isolated nucleic acid encoding the humanized immunoglobulin heavy chain of the humanized antibody MLN02, wherein said isolated nucleic acid comprises nucleotides 58-1410 of SEQ ID NO:10 and encodes amino acids 20-470 of SEQ ID NO:12.
8. The isolated nucleic acid of claim 7, wherein said isolated nucleic acid comprises nucleotides 76-1428 of SEQ ID NO:3.
9. The isolated nucleic acid of claim 7, wherein said isolated nucleic acid comprises nucleotides 77-1429 of SEQ ID NO:1.
10. A recombinant vector comprising the isolated nucleic acid of claim 1.
11. A recombinant vector comprising the isolated nucleic acid of claim 4.
12. A recombinant vector comprising the isolated nucleic acid of claim 7.
13. The recombinant vector of claim 11, wherein said isolated nucleic acid comprises nucleotides 79-735 of SEQ ID NO:2 or nucleotides 78-734 of SEQ ID NO:4.
14. The recombinant vector of claim 12, wherein said isolated nucleic acid comprises nucleotides 77-1429 of SEQ ID NO:1 or nucleotides 76-1428 of SEQ ID NO:3.
15. A recombinant vector encoding the humanized antibody MLN02, wherein said recombinant vector comprises a first nucleic acid that encodes the heavy chain and a second nucleic acid that encodes the light chain, wherein said first nucleic acid comprises nucleotides 77-1429 of SEQ ID NO:1, and said second nucleic acid comprises nucleotides 79-735 of SEQ ID NO:2.
16. A recombinant vector encoding the humanized antibody MLN02, wherein said recombinant vector comprises a first nucleic acid that encodes the heavy chain and a second nucleic acid that encodes the light chain, wherein said first nucleic acid comprises nucleotides 76-1428 of SEQ ID NO:3, and said second nucleic acid comprises nucleotides 78-734 of SEQ ID NO:4.
17. An isolated host cell comprising the isolated nucleic acid of claim 1.
18. An isolated host cell comprising the recombinant vector of claim 10.
19. An isolated host cell comprising the isolated nucleic acid of claim 4.
20. An isolated host cell comprising the recombinant vector of claim 11.
21. An isolated host cell comprising the isolated nucleic acid of claim 7.
22. An isolated host cell comprising the recombinant vector of claim 12.
23. An isolated host cell comprising the recombinant vector of claim 15.
24. An isolated host cell comprising the recombinant vector of claim 16.
25. A method of producing the humanized antibody MLN02 comprising maintaining a host cell of claim 17 under conditions suitable for expression of a nucleic acid encoding the MLN02 chains, whereby humanized antibody MLN02 chains are expressed and the humanized antibody MLN02 is produced.
26. A method of producing the humanized antibody MLN02 comprising providing the isolated nucleic acid of claim 1 and expressing said isolated nucleic acid, whereby the humanized light and heavy chains are expressed and the humanized antibody MLN02 is produced.
27. A method of producing the humanized antibody MLN02 comprising providing the recombinant vector of claim 15 and expressing said recombinant vector, whereby the humanized light and heavy chains are expressed and the humanized antibody MLN02 is produced.
28. A method of producing the humanized light chain of MLN02 comprising maintaining a host cell of claim 19 under conditions suitable for expression of the nucleic acid encoding the light chain of MLN02, whereby the humanized light chain of MLN02 is expressed and the humanized light chain of MLN02 is produced.
29. A method of producing the humanized light chain of MLN02 comprising providing the isolated nucleic acid of claim 4 and expressing said isolated nucleic acid, whereby the humanized light chain is expressed and the humanized light chain of MLN02 is produced.
30. A method of producing the humanized light chain of MLN02 comprising providing the recombinant vector of claim 11 and expressing said recombinant vector, whereby the humanized light chain is expressed and the humanized light chain of MLN02 is produced.
31. A method of producing the humanized heavy chain of MLN02 comprising maintaining a host cell of claim 21 under conditions suitable for expression of the nucleic acid encoding the heavy chain of MLN02, whereby the humanized heavy chain of MLN02 is expressed and the humanized heavy chain of MLN02 is produced.
32. A method of producing the humanized heavy chain of MLN02 comprising providing the isolated nucleic acid of claim 7 and expressing said isolated nucleic acid, whereby the humanized heavy chain is expressed and the humanized heavy chain of MLN02 is produced.
33. A method of producing the humanized heavy chain of MLN02 comprising providing the recombinant vector of claim 12 and expressing said recombinant vector, whereby the humanized heavy chain is expressed and the humanized heavy chain of MLN02 is produced.

1. A method for analyzing measurement errors in a set of measured signals {x(k)} measured in an experiment, wherein k=1, 2, . . . , N, N being the number of signals in said set, said method comprising
(a) transforming said set of measured signals using a transformation, said transformation transforming said set of measured signals into a set of transformed signals {y(k)} such that said transformed signals have a near constant error; and
(b) determining error in each of said transformed signals by normalizing measurement error in said measured signal by a modeled error in said measured signal, said modeled error being calculated using an error model of said experiment; and
(c) outputting said set of transformed signals andor said errors determined in step (b) to a user interface device, a computer readable storage medium, or a local or remote computer system; or displaying said set of transformed signals andor said errors determined in step (b).
2. The method of claim 1, wherein said error model is a three-term error model according to the equation
\u03c3x(k)=\u221a{square root over (c2+b2\xb7x(k)+a2\xb7x(k)2)}{square root over (c2+b2\xb7x(k)+a2\xb7x(k)2)}
wherein a is the fractional error coefficient of said experiment, b is the Poisson error coefficient of said experiment, and c is the standard deviation of background noise of said experiment.
3. The method of claim 2, wherein for each measured signal x(k) said transformation is carried out according to the equation
y
\u2061

(
k
)
=
ln
\u2061

(
b
2

+

2
\xb7

a
2

\xb7

x
\u2061

(
k
)
a

+

2
\xb7
c
2

+
b
2

\xb7

x
\u2061

(
k
)
+
a
2

\xb7
x
\u2061

(
k
)
2
)
a

+
d
,
wherein
d
=
–

ln
\u2061

(
b
2

a

+

2
\xb7
c
)
a

.
4. The method of claim 3, wherein for each measured signal x(k) said error of said transformed signal y(k) is determined according to the equation
\u0394
\u2062
\u2062

y
\u2061

(
k
)
=
\u0394
\u2062
\u2062

x
\u2061

(
k
)
c
2

+
b
2

\xb7

x
\u2061

(
k
)
+
a
2

\xb7
x
\u2061

(
k
)
2
wherein \u0394y(k) is said error in said transformed signal y(k), and wherein \u0394x(k) is an error in said measured signal x(k).
5. The method of claim 4, wherein for each measured signal x(k) said error in said measured signal x(k) is an error in said measured signal x(k) determined in said experiment.
6. The method of claim 5, wherein for each measured signal x(k) said error in said measured signal x(k) is the larger of an error in said measured signal x(k) determined in said experiment or said modeled error in said measured signal x(k).
7. The method of claim 1, further comprising before said step (a) the steps of
(i) determining said error model for said experiment;
(ii) determining said modeled error in each of said measured signals; and
(iii) determining said transformation for said experiment.
8. The method of claim 7, wherein said error model is a three-term error model according to the equation
\u03c3x(k)=\u221a{square root over (c2+b2\xb7x(k)+a2\xb7x(k)2)}{square root over (c2+b2\xb7x(k)+a2\xb7x(k)2)}
wherein a is the fractional error coefficient of said experiment, b is the Poisson error coefficient of said experiment, and c is the standard deviation of background noise of said experiment.
9. The method of claim 8, wherein for each measured signal x(k) said transformation is carried out according to the equation
y
\u2061

(
k
)
=
ln
\u2061

(
b
2

+

2
\xb7

a
2

\xb7

x
\u2061

(
k
)
a

+

2
\xb7
c
2

+
b
2

\xb7

x
\u2061

(
k
)
+
a
2

\xb7
x
\u2061

(
k
)
2
)
a

+
d
,
wherein
d
=
–

ln
\u2061

(
b
2

a

+

2
\xb7
c
)
a

.
10. The method of claim 9, wherein for each measured signal x(k) said error of said transformed signal y(k) is determined according to the equation
\u0394
\u2062
\u2062

y
\u2061

(
k
)
=
\u0394
\u2062
\u2062

x
\u2061

(
k
)
c
2

+
b
2

\xb7

x
\u2061

(
k
)
+
a
2

\xb7
x
\u2061

(
k
)
2
wherein \u0394y(k) is said error in said transformed signal y(k), and wherein \u0394x(k) is an error in said measured signal x(k).
11. The method of claim 10, wherein for each measured signal x(k) said error in said measured signal x(k) is an error in said measured signal x(k) determined in said experiment.
12. The method of claim 10, wherein for each measured signal x(k) said error in said measured signal x(k) is the larger of an error in said measured signal x(k) determined in said experiment or said modeled error in said measured signal x(k).
13. A method for determining a residue error in a measured signal x measured in an experiment, said method comprising
(a) transforming said measured signal into a transformed signal y by a method comprising using a transformation according to the equation
y
=
ln
\u2061

(
b
2

+

2
\xb7

a
2

\xb7
x
a

+

2
\xb7
c
2

+
b
2

\xb7
x

+
a
2

\xb7

x
2
)
a

+
d
,
wherein a is the fractional error coefficient of said experiment, b is the Poisson error coefficient of said experiment, and c is the standard deviation of background noise of said experiment, and
d
=
–

ln
\u2061

(
b
2

a

+

2
\xb7
c
)
a
(b) determining an error in said transformed signal y;
(c) determining said residue error by subtracting 1 from said error of said transformed signal y; and
(d) outputting said residue error to a user interface device, a computer readable storage medium, or a local or remote computer system; or displaying said residue error.
14. The method of claim 13, wherein said error of said transformed signal y is determined according to the equation
\u0394
\u2062
\u2062
y

=
\u0394
\u2062
\u2062
x
c
2

+
b
2

\xb7
x

+
a
2

\xb7

x
2
;
wherein \u0394y is said error in said transformed signal y, and wherein \u0394x is an error in said measured signal x.
15. The method of claim 14, wherein said error in said measured signal x is an error in said measured signal x determined in said experiment.
16. The method of claim 14, wherein said error in said measured signal x is the larger of an error determined in said experiment or an error calculated according to the equation
\u0394x=\u221a{square root over (c2+b2\xb7x+a2\xb7x2)}.
17. A method for determining an error-weighted average of m signals xi measured in m replicate experiments, wherein i=1, 2, . . . m, said method comprising
(a) transforming each said signal xi into a transformed signal yi by a method comprising using a transformation
y
i

=
ln
\u2061

(
b
2

+

2
\xb7

a
2

\xb7

x
i
a

+

2
\xb7
c
2

+
b
2

\xb7

x
i
+
a
2

\xb7

x
i
2
)
a

+
d
,
wherein a is the fractional error coefficient of said experiments, b is the Poisson error coefficient of said experiments, and c is the averaged standard deviation of background noise of said experiments, and
d
=
–

ln
\u2061

(
b
2

a

+

2
\xb7
c
)
a
;
(b) determining an error \u0394yi in each said transformed signal y1;
(c) determining an error-weighted transformed signal according to the equation
y
_

=
\u2211
i

\u2062

(
y
i
\u0394

\u2062
\u2062

y
i
2
)
\u2211
i

\u2062

(
1

\u0394

\u2062
\u2062

y
i
2
)
;
(d) transforming said error-weighted transformed signal to produce said error-weighted average by a method comprising using a transformation according to the equation
x
_

=
–

(
4
\xb7

a
2

\xb7

c
2
–
a
2

\xb7

\u2147

2
\u2062

a
\xb7

(
y
_

–
d

)
+

2
\xb7
a
\xb7

b
2

\xb7

\u2147

a
\xb7

(
y
_

–
d

)
–

b
4
)
4
\xb7

a
3

\xb7

\u2147

a
\xb7

(
y
_

–
d

)
;
and
(e) outputting said error-weighted average x to a user interface device, a computer readable storage medium, or a local or remote computer system; or displaying said error-weighted average x.
18. The method of claim 17, wherein said error \u0394yi of each said transformed signal yi is obtained according to the equation
\u0394
\u2062
\u2062

y
i
=
\u0394
\u2062
\u2062

x
i
c
2

+
b
2

\xb7

x
i
+
a
2

\xb7

x
i
2
wherein \u0394xi is an error in said measured signal xi.
19. The method of claim 18, wherein each said error \u0394xi is an error in said measured signal xi determined in said ith experiment.
20. The method of claim 18, wherein each said error \u0394xi is the larger of an error in said measured signal xi determined in said ith experiment or an error in said measured signal xi calculated according to the equation
\u0394xi=\u221a{square root over (c2+b2\xb7xi+a2\xb7xi2)}.
21. A method for analyzing m signals {x}i measured in m experiments, wherein i=1, 2, . . . m, said method comprising
(a) transforming each said signal xi into a transformed signal yi by a method comprising using a transformation
y
i

=
ln
\u2061

(
b
2

+

2
\xb7

a
2

\xb7

x
i
a

+

2
\xb7
c
2

+
b
2

\xb7

x
i
+
a
2

\xb7

x
i
2
)
a

+
d
,
wherein a is the fractional error coefficient of said experiments, b is the Poisson error coefficient of said experiments, c is the averaged standard deviation of background noise of said experiments, and
d
=
–

ln
\u2061

(
b
2

a

+

2
\xb7
c
)
a
;
(b) determining an error \u0394yi in each said transformed signal yi;
(c) analyzing said transformed signals and their errors, thereby analyzing said m signals; and
(d) outputting said transformed signal yi andor said error \u0394yi to a user interface device, a computer readable storage medium, or a local or remote computer system; or displaying said transformed signal yi andor said error \u0394yi.
22. The method of claim 21, wherein for each measured signal xi said error in said transformed signal yi is obtained according to the equation
\u0394
\u2062
\u2062

y
i
=
\u0394
\u2062
\u2062

x
i
c
2

+
b
2

\xb7

x
i
+
a
2

\xb7

x
i
2
wherein \u0394yi is said error in said transformed signal yi, and wherein \u0394xi is an error in said measured signal xi.
23. The method of claim 22, wherein said error in said measured signal xi is an error in said measured signal xi determined in said ith experiment.
24. The method of claim 22, wherein said error in measured signal xi is the larger of an error in said measured signal xi determined in said ith experiment or an error in said measured signal xi calculated according to the equation
\u0394xi=\u221a{square root over (c2+b2\xb7xi+a2\xb7xi2)}.
25. The method of claim 24, wherein said step (c) is carried out by a method comprising performing ANOVA on said transformed signals and their errors.
26. The method of claim 25, wherein said step (c) is carried out by a method comprising performing a regression analysis on said transformed signals and their errors.
27. The method of claim 25, wherein said step (c) is carried out by a method comprising determining a variance of said transformed signals.
28. A method for determining if a measured signal x measured in an experiment is an outlier, comprising
(a) transforming said measured signal into a transformed signal y by a method comprising using a transformation according to the equation
y
=
ln
\u2061

(
b
2

+

2
\xb7

a
2

\xb7
x
a

+

2
\xb7
c
2

+
b
2

\xb7
x

+
a
2

\xb7

x
2
)
a

+
d
\u2062
,
wherein a is the fractional error coefficient of said experiment, b is the Poisson error coefficient of said experiment, and c is the standard deviation of background noise of said experiment, and
d
=
–

ln
\u2061

(
b
2

a

+

2
\xb7
c
)
a
\u2062
;
(b) determining an error in said transformed signal y;
(c) comparing said error in said transformed signal y with a predetermined threshold value, wherein said measured signal is identified as an outlier if said error in said transformed signal y is greater than said threshold value; and
(d) outputting a result of said comparing step (c) to a user interface device, a computer readable storage medium, or a local or remote computer system; or displaying a result of said comparing step (c).
29. The method of claim 28, wherein said error of said transformed signal y is determined according to the equation
\u0394
\u2062
\u2062
y

=
\u0394
\u2062
\u2062
x
c
2

+
b
2

\xb7
x

+
a
2

\xb7

x
2
wherein \u0394y is said error in said transformed signal y, and wherein \u0394x is an error in said measured signal x.
30. The method of claim 28, wherein said error in measured signal x(k) is the larger of an error in said measured signal x determined in said experiment or an error in said measured signal x calculated according to the equation
\u0394x=\u221a{square root over (c2+b2\xb7x+a2\xb7x2)}.
31. A method of obtaining a difference of measured signals {x(k)} measured in an experiment, wherein k=1, 2, said method comprising
(a) transforming said signals into transformed signals {y(k)} by transforming each of said measured signals by a method comprising using a transformation according to
y
\u2061

(
k
)
=
ln
\u2061

(
b
2

+

2
\xb7

a
2

\xb7

x
\u2061

(
k
)
a

+

2
\xb7
c
2

+
b
2

\xb7

x
\u2061

(
k
)
+
a
2

\xb7
x
\u2061

(
k
)
2
)
a

+
d
\u2062
,
wherein a is the fractional coefficient of said experiment, b is the Poisson coefficient of said experiment, and c is the standard deviation of background noise of said experiment, and
d
=
–

ln
\u2061

(
b
2

a

+

2
\xb7
c
)
a
\u2062
;
(b) determining a difference between said transformed signals; and
(c) outputting said difference to a user interface device, a computer readable storage medium, or a local or remote computer system; or displaying said difference.
32. The method of any one of claims 1, 7, 13, 17, 21, 28 and 31, wherein said experiment is an experiment in which measurements of a plurality of cellular constituents are obtained.
33. The method of claim 32, wherein said experiment is a microarray experiment and said cellular constituents are mRNAs.
34. The method of claim 32, wherein said cellular constituents are proteins.
35. The method of claim 32, wherein said measured signal is measured fluorescence intensity.
36. A computer program product for use in conjunction with a computer having a processor and a memory connected to the processor, said computer program product comprising a computer readable storage medium having a computer program mechanism encoded thereon, wherein said computer program mechanism may be loaded into the memory of said computer and cause said computer to carry out the method of claim 35.
37. A computer system comprising
a processor, and
a memory coupled to said processor and encoding one or more programs, wherein said one or more programs cause the processor to carry out the method of claim 35.
38. The method of claim 32, wherein said measured signal is a difference between a measured fluorescence intensity of a probe and a measured fluorescence intensity of a reference probe.
39. A computer system comprising
a processor, and
a memory coupled to said processor and encoding one or more programs, wherein said one or more programs cause the processor to carry out the method of claim 38.
40. A computer program product for use in conjunction with a computer having a processor and a memory connected to the processor, said computer program product comprising a computer readable storage medium having a computer program mechanism encoded thereon, wherein said computer program mechanism may be loaded into the memory of said computer and cause said computer to carry out the method of claim 38.
41. A computer system comprising
a processor, and
a memory coupled to said processor and encoding one or more programs, wherein said one or more programs cause the processor to carry out the method of any one of claims 1, 7, 13, 17, 21, 28 and 31.
42. A computer program product for use in conjunction with a computer having a processor and a memory connected to the processor, said computer program product comprising a computer readable storage medium having a computer program mechanism encoded thereon, wherein said computer program mechanism may be loaded into the memory of said computer and cause said computer to carry out the method of any one of claims 1, 7, 13, 17, 21, 28 and 31.

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 for detecting breakage of a diesel particulate filter (DPF), the method comprising:
obtaining a learned value of a flow resistance in the DPF when a regeneration of the DPF is terminated;
calculating a predicted value of the flow resistance;
determining whether the learned value is within a permissible error range of the predicted value; and
determining that the DPF is broken if the learned value is not within the permissible error range.
2. The method of claim 1, further comprising:
storing an arithmetic average of the learned value according to a mileage of a vehicle; and
transforming a relation between the mileage and the learned value to a polynomial expression.
3. The method of claim 2, wherein the predicted value is calculated by using the polynomial expression.
4. The method of claim 1, which is reiterated if the learned value is within the permissible error range.