Commit a44ef145 authored by Martin Larralde's avatar Martin Larralde
Browse files

Add eigendecomposition tables for Nakashima (1986) dataset

parent 667f7c55
......@@ -4,6 +4,7 @@
### Added manually
# Build files
peptides/tables/__init__.py
peptides/datasets/__init__.py
### C ###
# Prerequisites
......
......@@ -151,15 +151,16 @@ class Peptide(typing.Sequence[str]):
# fmt: off
_CODE1 = [
"A", "R", "N", "D", "C", "Q", "E", "G", "H", "I", "L", "K", "M", "F",
"P", "S", "T", "W", "Y", "V", "O", "U", "B", "Z", "J", "X"
"A", "R", "N", "D", "C", "Q", "E", "G", "H", "I",
"L", "K", "M", "F", "P", "S", "T", "W", "Y", "V",
"O", "U", "B", "Z", "J", "X"
]
# fmt: off
_CODE3 = [
"Ala", "Arg", "Asn", "Asp", "Cys", "Gln", "Glu", "Gly", "His", "Ile",
"Leu", "Lys", "Met", "Phe", "Pro", "Pyl", "Ser", "Sec", "Thr", "Trp",
"Tyr", "Val", "Asx", "Glx", "Xaa", "Xle",
"Leu", "Lys", "Met", "Phe", "Pro", "Ser", "Thr", "Trp", "Tyr", "Val",
"Pyl", "Sec", "Asx", "Glx", "Xle", "Xaa"
]
# --- Class methods ------------------------------------------------------
......@@ -1263,7 +1264,7 @@ class Peptide(typing.Sequence[str]):
def structural_class(
self,
frequencies: str = "ChouZhang",
frequencies: str = "Nakashima",
distance: str = "mahalanobis",
) -> str:
"""Predict the structural class of the peptide from its sequence.
......@@ -1281,8 +1282,9 @@ class Peptide(typing.Sequence[str]):
centroids. Use `"Chou"` to load the frequencies of the 64
proteins analyzed in Chou (1989), `"Nakashima"` to use
the normalized frequencies of the 135 proteins analyzed in
Nakashima *et al* (1986), or `"ChouZhang"` to load the
frequencies of 120 proteins used in Chou & Zhang (1995).
Nakashima *et al* (1986) and Zhang & Chou (1995), or
`"ChouZhang"` to load the frequencies of 120 proteins used
in Chou & Zhang (1995).
distance (`str`): The distance metric to use in the 20-D space
formed by the 20 usual amino acid to find the nearest
structural class for the peptide. Use `"cityblock"` to use
......@@ -1295,8 +1297,9 @@ class Peptide(typing.Sequence[str]):
Returns:
`str`: The structural class the protein most likely belongs to.
Note that some classes may not be predictable, depending on the
refernce frequencies being used (at the moment, only
*Nakashima* supports the ζ class).
reference frequencies being used (at the moment, the ζ class
can only be predicted from the *Nakashima* frequencies with
*euclidean* or *manhattan* distances).
Example:
Predict the structural class of the skipjack tuna Cytochrome C,
......@@ -1308,6 +1311,8 @@ class Peptide(typing.Sequence[str]):
... "DANKSKGIVWNENTLMEYLENPKKYIPGTKMIFAGIKKKGERQDLVAYLK"
... "SATS"
... )
>>> p.structural_class("Nakashima", distance="mahalanobis")
'alpha'
>>> p.structural_class("ChouZhang", distance="mahalanobis")
'beta'
>>> p.structural_class("Chou", distance="correlation")
......@@ -1325,6 +1330,8 @@ class Peptide(typing.Sequence[str]):
... "MKTLLLTLVVVTIVCLDLGYTRICFNHQSSQPQTTKTCSPGESSCYHKQW"
... "SDFRGTIIERGCGCPTVKPGIKLSCCESEVCNN"
... )
>>> p.structural_class("Nakashima", distance="mahalanobis")
'beta'
>>> p.structural_class("ChouZhang", distance="mahalanobis")
'alpha+beta'
>>> p.structural_class("Chou", distance="correlation")
......@@ -1342,14 +1349,8 @@ class Peptide(typing.Sequence[str]):
... "MATYKVTLINEAEGINETIDCDDDTYILDAAEEAGLDLPYSCRAGACSTC"
... "AGTITSGTIDQSDQSFLDDDQIEAGYVLTCVAYPTSDCTIKTHQEEGLY"
... )
>>> p.structural_class("ChouZhang", distance="mahalanobis")
'alpha'
>>> p.structural_class("Chou", distance="correlation")
'alpha+beta'
>>> p.structural_class("Nakashima", distance="euclidean")
'zeta'
>>> p.structural_class("Chou", distance="cityblock")
'alpha+beta'
References:
- Chou, K-C., and C-T. Zhang.
......@@ -1371,6 +1372,11 @@ class Peptide(typing.Sequence[str]):
*The Folding Type of a Protein Is Relevant to the Amino Acid
Composition*. Journal of Biochemistry. Jan 1986;99(1):153–62.
doi:10.1093/oxfordjournals.jbchem.a135454. PMID:3957893.
- Zhang, Chun-Ting, and Kuo-Chen Chou.
*An Eigenvalue-Eigenvector Approach to Predicting Protein
Folding Types*.
Journal of Protein Chemistry. Jul 1995;14(5):309–26.
doi:10.1007/BF01886788. PMID:8590599.
- Zhou, G.P., and N. Assa-Munt.
*Some Insights into Protein Structural Class Prediction*.
Proteins: Structure, Function, and Bioinformatics.
......@@ -1423,7 +1429,7 @@ class Peptide(typing.Sequence[str]):
s = sum((pep_frequencies[x]-table[x])**2 for x in table)
distances[name] = math.sqrt(s)
elif distance == "mahalanobis":
x = [pep_frequencies[aa] for aa in sorted(self._CODE1[:20])]
x = [pep_frequencies[aa]*100 for aa in sorted(self._CODE1[:20])]
for name,tables in dataset.items():
if name == "all":
continue
......@@ -1432,22 +1438,25 @@ class Peptide(typing.Sequence[str]):
eivals = tables.get("eigenvalues")
eivecs = tables.get("eigenvectors")
if eivals is None or eivecs is None:
raise ValueError(
f"Cannot use {frequencies!r} frequencies with "
f"{distance!r} distance (no eigendecomposition available)"
)
continue
# compute Mahalanobis distance using the components of
# the eigendecomposition
xm = [mean[aa] for aa in sorted(self._CODE1[:20])]
xm = [mean[aa]*100 for aa in sorted(self._CODE1[:20])]
y = [
sum(eivecs[i][j] * (x[j] - xm[j]) for j in range(20))
for i in range(20)
]
distances[name] = sum(y[i]**2 / eivals[i] for i in range(1, 20))
if not distances:
raise ValueError(
f"Cannot use {frequencies!r} frequencies with "
f"{distance!r} distance (no eigendecomposition available)"
)
else:
raise ValueError(f"Invalid distance: {distance!r}")
# find the most likely structural class based on the distance
# print(distances)
return min(distances, key=distances.get)
# --- Descriptors --------------------------------------------------------
......
......@@ -12,3 +12,8 @@ Nakashima, H., K. Nishikawa, and T. Ooi.
*The Folding Type of a Protein Is Relevant to the Amino Acid Composition*.
Journal of Biochemistry. Jan 1986;99(1):158, Table II.
doi:10.1093/oxfordjournals.jbchem.a135454. PMID:3957893.
Chou, K-C., and C-T. Zhang.
*Prediction of Protein Structural Classes*.
Critical Reviews in Biochemistry and Molecular Biology. Feb 1995;30:275–349. Appendix C.
doi:10.3109/10409239509083488. PMID:7587280.
# eig A C D E F G H I K L M N P Q R S T V W Y
0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2.5 -0.12 -0.08 -0.38 0.04 0.11 -0.13 -0.20 0.04 0.01 -0.05 0.41 -0.09 -0.04 -0.43 -0.09 0.02 0.10 0.07 0.55 0.26
3.4 0.02 -0.02 -0.35 -0.03 0.06 -0.05 0.60 0.26 -0.07 -0.09 -0.32 0.15 -0.12 -0.37 0.18 -0.14 0.18 0.26 -0.11 -0.04
6.3 -0.08 -0.11 0.03 -0.08 -0.29 0.18 0.90 0.20 -0.02 0.00 0.63 0.08 0.21 -0.28 0.02 0.10 -0.23 -0.09 -0.43 -0.05
8.8 0.22 -0.17 -0.35 0.20 -0.06 0.03 -0.40 0.11 -0.08 0.34 -0.22 0.33 0.10 -0.11 0.21 0.22 -0.28 0.29 0.80 0.18
25.3 0.21 -0.14 -0.10 0.18 -0.47 -0.14 0.32 -0.16 -0.22 -0.14 0.01 0.16 0.33 0.20 0.02 -0.12 0.01 -0.33 0.40 -0.05
32.0 0.12 0.31 0.02 -0.20 -0.34 0.26 0.21 -0.06 0.18 0.36 -0.04 -0.32 -0.22 -0.15 -0.34 -0.03 0.30 -0.24 0.05 0.14
41.5 0.02 0.04 0.08 -0.09 -0.26 -0.18 -0.05 0.32 0.18 0.09 -0.36 -0.30 0.23 -0.15 0.21 0.25 -0.48 0.04 0.14 0.29
47.5 0.18 0.18 -0.29 -0.03 0.45 0.04 0.16 0.37 -0.21 -0.01 0.04 -0.19 0.14 0.25 -0.41 0.01 -0.26 -0.28 -0.04 -0.01
69.2 -0.01 -0.08 0.22 -0.27 0.08 0.17 -0.17 0.23 0.07 -0.08 -0.19 0.06 0.50 -0.28 -0.21 0.01 0.26 -0.01 0.20 -0.48
82.8 0.05 -0.01 -0.11 0.10 0.60 0.16 0.07 -0.08 0.08 -0.25 0.02 -0.32 -0.17 -0.07 0.44 0.49 0.11 -0.41 0.04 -0.31
87.7 0.14 0.24 0.40 0.20 -0.14 -0.42 -0.14 0.41 -0.40 -0.08 0.11 -0.09 -0.23 -0.12 0.06 -0.03 0.26 -0.06 -0.05 -0.06
108.8 -0.14 0.23 -0.04 0.35 -0.17 0.32 -0.08 0.80 -0.01 -0.09 -0.09 0.33 0.35 -0.08 -0.14 0.05 -0.40 0.01 0.27 -0.35
146.6 0.16 -0.16 0.31 0.14 0.09 0.06 0.08 0.02 0.29 -0.54 -0.12 0.23 -0.15 -0.20 -0.32 0.06 -0.05 -0.23 -0.05 0.38
197.1 -0.01 -0.02 -0.32 -0.06 -0.38 0.13 -0.19 0.36 0.20 -0.42 0.04 -0.10 -0.08 0.42 -0.09 0.09 0.23 0.27 -0.06 0.00
292.3 -0.19 0.31 -0.08 0.63 -0.02 0.12 -0.05 -0.18 0.03 -0.16 -0.09 -0.31 0.40 -0.16 -0.07 -0.18 0.08 0.10 -0.22 0.04
377.6 -0.32 -0.16 0.04 0.18 -0.05 -0.42 0.25 -0.08 0.10 0.17 -0.04 0.00 0.02 0.07 -0.40 0.58 0.10 0.14 -0.04 -0.13
457.9 -0.20 -0.51 0.19 0.13 0.02 0.46 0.07 0.15 -0.48 0.08 -0.11 -0.27 -0.07 0.08 -0.04 0.05 0.06 0.08 0.10 0.22
520.7 -0.55 -0.14 0.03 0.11 0.07 -0.12 0.05 0.33 0.35 0.15 0.01 0.04 -0.02 0.16 0.16 -0.37 0.14 -0.43 0.02 0.02
894.2 0.49 -0.45 0.01 0.31 0.02 -0.10 0.00 0.07 0.34 0.20 0.10 -0.28 -0.12 -0.04 -0.07 -0.25 -0.09 0.15 -0.02 -0.29
# eig A C D E F G H I K L M N P Q R S T V W Y
0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
4.5 0.12 -0.03 -0.03 0.04 0.04 0.07 0.03 0.14 -0.03 0.03 -0.22 -0.02 0.37 -0.10 0.11 -0.11 -0.04 0.00 -0.76 0.40
14.3 0.10 0.09 -0.13 0.37 -0.03 0.15 0.33 0.05 -0.37 -0.08 0.10 0.44 -0.02 -0.37 -0.04 -0.1 0.16 -0.27 -0.09 -0.31
14.7 -0.02 -0.62 0.04 -0.07 -0.16 0.13 0.11 0.01 0.27 -0.03 -0.13 -0.01 0.29 -0.18 0.49 0.03 0.09 -0.06 0.13 -0.29
21.2 0.01 0.30 -0.09 -0.24 -0.14 -0.20 -0.20 0.47 0.20 0.28 -0.21 0.32 0.17 -0.30 -0.03 -0.09 -0.28 0.03 0.15 -0.17
32.4 -0.10 -0.07 0.03 -0.14 -0.01 0.08 -0.03 0.22 0.15 0.12 0.22 0.12 -0.28 0.29 -0.03 -0.03 0.17 0.31 -0.50 -0.51
66.9 -0.09 -0.17 0.11 -0.11 -0.18 -0.13 0.29 0.12 -0.05 -0.14 0.07 0.21 0.21 0.33 -0.29 0.53 -0.36 -0.26 -0.08 -0.02
70.1 0.02 0.10 -0.03 0.12 -0.30 0.24 0.01 0.26 -0.04 -0.16 -0.69 -0.03 -0.30 0.18 0.00 0.23 0.26 0.04 0.06 0.04
85.5 0.14 -0.17 -0.49 -0.26 0.24 -0.16 0.09 0.01 0.22 0.23 -0.06 0.15 -0.10 0.19 -0.13 -0.05 0.39 -0.43 0.02 0.15
98.1 -0.04 0.14 -0.18 0.08 0.31 0.21 0.12 0.31 -0.11 -0.09 -0.08 -0.44 0.31 0.33 -0.02 -0.28 -0.16 -0.19 0.12 -0.33
117.5 0.02 0.06 0.03 0.02 -0.03 -0.07 -0.17 -0.49 -0.10 0.02 -0.31 0.39 0.42 0.37 -0.12 -0.20 0.12 0.18 0.04 -0.20
153.7 -0.10 0.14 -0.14 0.02 0.53 0.23 -0.24 -0.21 0.18 -0.20 -0.16 0.24 -0.17 -0.05 0.28 0.33 -0.32 -0.10 -0.12 -0.11
170.4 -0.02 -0.26 -0.14 0.01 0.05 0.00 0.32 0.10 -0.03 -0.14 -0.07 0.32 -0.29 0.21 0.14 -0.45 -0.40 0.28 0.12 0.26
204.8 -0.15 -0.24 0.45 -0.03 0.05 0.39 -0.43 0.19 -0.11 0.10 0.06 0.21 -0.07 0.09 -0.15 -0.24 0.02 -0.38 0.06 0.16
277.4 -0.05 -0.21 -0.49 0.25 -0.09 0.08 -0.44 0.23 0.01 -0.35 0.27 0.09 0.21 0.00 -0.18 0.12 0.09 0.26 0.09 0.12
311.6 0.15 0.24 0.00 -0.06 -0.29 -0.28 -0.22 0.08 -0.16 -0.26 0.22 0.08 -0.05 0.31 0.59 -0.08 0.05 -0.30 -0.05 0.02
372.4 -0.03 0.13 -0.36 0.03 -0.47 0.50 0.01 -0.26 0.07 0.43 0.16 -0.06 -0.02 0.12 0.07 0.01 -0.26 -0.10 -0.02 0.05
647.0 -0.66 0.26 -0.06 -0.44 -0.03 0.19 0.22 -0.01 -0.04 -0.23 0.08 0.10 0.16 -0.10 0.08 -0.05 0.26 0.05 0.03 0.16
739.3 -0.16 0.17 0.12 0.38 -0.17 -0.09 0.08 -0.08 0.71 -0.27 0.03 0.00 -0.01 0.01 -0.17 -0.26 0.00 -0.22 -0.08 0.01
1195.5 0.60 0.09 0.07 -0.47 -0.17 0.35 0.05 -0.08 0.15 -0.41 0.09 0.01 0.03 -0.10 -0.20 -0.09 -0.02 0.04 0.02 0.04
# eig A C D E F G H I K L M N P Q R S T V W Y
0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
10.6 0.12 -0.10 -0.25 0.05 0.16 0.06 0.18 -0.03 -0.15 0.23 0.13 0.15 0.01 -0.29 -0.35 -0.34 -0.17 0.17 -0.17 0.57
12.0 -0.10 -0.50 0.12 -0.17 0.16 0.21 -0.10 -0.03 -0.02 0.60 0.22 0.25 -0.04 -0.53 0.16 0.01 0.22 -0.02 0.24 -0.24
15.2 0.00 -0.40 0.20 0.17 0.13 0.03 0.33 -0.01 -0.07 0.03 0.35 -0.15 0.14 0.21 -0.18 0.27 -0.01 -0.24 -0.51 -0.18
20.7 -0.13 0.28 0.06 0.18 -0.12 0.11 0.60 -0.30 -0.06 0.09 -0.27 0.14 0.11 -0.22 -0.27 -0.04 0.08 -0.16 0.20 0.29
22.8 0.07 0.43 0.04 -0.15 -0.18 -0.08 0.11 -0.15 0.60 -0.05 0.38 0.22 -0.29 -0.29 0.34 0.00 0.01 -0.12 -0.44 -0.01
29.2 0.00 0.16 -0.17 -0.01 0.11 -0.21 0.05 -0.10 -0.08 -0.03 0.61 -0.32 -0.23 0.08 -0.22 -0.06 0.02 -0.10 0.50 -0.10
32.7 0.18 0.05 -0.06 0.06 0.64 -0.15 0.12 0.07 -0.04 0.01 -0.33 0.01 -0.55 -0.04 0.00 0.15 0.05 0.14 -0.11 -0.18
37.8 -0.10 0.26 0.23 0.24 0.00 0.07 -0.51 -0.35 -0.33 0.17 0.13 0.17 0.04 0.10 -0.26 -0.02 0.20 0.23 -0.19 -0.16
58.6 -0.04 0.29 0.20 -0.29 0.24 0.08 -0.10 -0.01 0.01 0.29 -0.07 -0.53 0.28 -0.36 -0.03 0.28 -0.19 -0.08 0.05 0.08
78.1 -0.20 0.03 0.27 -0.34 -0.26 -0.08 0.27 0.43 -0.36 0.17 0.05 -0.14 -0.18 0.07 -0.07 -0.13 0.22 0.38 0.07 -0.07
86.5 -0.16 -0.16 0.40 -0.01 -0.09 0.28 0.11 -0.38 -0.02 0.02 0.00 -0.04 -0.41 0.19 0.12 0.11 -0.47 0.12 0.16 0.22
110.7 0.03 -0.13 -0.08 0.08 -0.28 0.01 -0.07 -0.19 0.26 0.37 -0.15 -0.26 -0.31 0.02 -0.09 0.15 0.56 -0.16 -0.04 0.29
122.4 0.20 -0.16 0.26 0.23 -0.08 -0.44 0.12 -0.27 0.09 -0.37 -0.03 -0.20 0.18 -0.30 0.14 0.09 0.16 0.36 0.03 0.17
160.6 -0.21 0.02 -0.04 -0.26 0.32 0.31 0.13 -0.28 0.26 -0.31 0.03 -0.24 0.11 0.17 0.06 -0.42 0.31 0.18 -0.13 0.00
206.4 -0.17 0.07 0.46 0.09 0.17 -0.09 -0.07 0.21 0.02 -0.38 -0.01 0.20 -0.09 -0.05 -0.34 0.01 0.80 -0.43 0.06 0.35
275.2 0.52 -0.08 0.42 -0.04 0.02 -0.12 -0.03 -0.04 0.04 0.27 -0.10 -0.07 0.04 0.12 0.10 -0.57 -0.06 -0.27 -0.01 -0.13
290.0 0.10 0.09 -0.07 0.36 -0.03 0.48 -0.02 0.18 -0.50 -0.21 -0.03 -0.28 -0.11 -0.14 0.30 -0.10 0.13 -0.24 -0.01 0.10
374.0 -0.39 -0.01 -0.11 -0.06 0.24 -0.39 0.11 -0.19 -0.35 0.25 -0.04 0.10 0.14 0.20 0.44 -0.05 0.08 -0.26 0.06 0.22
484.8 0.48 -0.02 -0.12 -0.55 -0.05 0.15 0.06 -0.26 -0.35 -0.14 -0.08 0.19 0.09 0.17 -0.07 0.30 0.14 -0.05 0.04 0.08
# eig A C D E F G H I K L M N P Q R S T V W Y
0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
6.6 -0.08 -0.22 -0.25 -0.22 0.41 0.11 -0.17 0.02 0.21 -0.12 -0.07 -0.11 0.09 -0.08 0.02 -0.06 -0.05 -0.09 0.72 -0.08
11.9 0.04 0.13 -0.15 0.01 0.48 -0.23 0.27 0.07 -0.08 -0.04 -0.45 0.10 -0.31 0.02 -0.21 -0.20 0.06 0.20 -0.10 0.38
16.9 0.02 -0.21 0.36 0.04 -0.28 0.05 -0.11 -0.10 0.29 -0.15 -0.67 0.10 -0.05 0.13 0.33 0.09 -0.05 0.05 0.08 0.08
19.8 0.02 0.22 0.15 0.34 -0.08 -0.13 -0.32 0.00 -0.04 0.04 0.11 -0.36 -0.59 0.11 -0.03 0.02 0.13 0.24 0.30 -0.13
31.5 0.00 -0.13 -0.10 0.13 0.21 -0.02 -0.74 0.14 0.14 -0.20 0.13 0.31 0.09 0.08 -0.03 -0.09 0.10 0.16 -0.32 0.14
43.6 0.04 -0.14 0.08 -0.29 -0.31 -0.11 0.16 0.11 0.25 -0.43 0.39 0.05 -0.24 -0.07 -0.13 0.01 -0.09 0.10 0.12 0.47
51.5 0.11 0.03 -0.02 -0.20 0.34 0.01 0.02 -0.02 0.44 0.06 0.10 -0.34 -0.28 -0.19 0.40 0.21 -0.06 -0.16 -0.40 -0.06
64.7 0.13 0.20 -0.06 -0.20 -0.24 0.00 -0.24 0.60 -0.04 0.31 -0.22 -0.23 0.16 -0.33 -0.05 0.03 -0.17 0.13 0.03 0.19
87.3 -0.01 0.30 -0.15 -0.08 -0.01 0.21 0.15 0.25 0.08 -0.32 -0.05 -0.39 0.16 0.57 -0.06 -0.13 -0.17 0.32 -0.07 -0.24
92.8 -0.06 0.34 0.24 -0.48 0.13 -0.04 -0.16 -0.36 -0.10 0.13 0.07 0.19 0.01 0.07 0.04 0.05 -0.42 0.40 0.05 -0.07
151.3 0.15 -0.01 -0.21 0.35 0.08 -0.10 0.03 -0.02 -0.41 -0.36 0.03 -0.05 0.10 -0.16 0.42 0.28 -0.41 0.10 0.06 0.14
211.0 -0.07 -0.07 0.23 -0.13 0.09 0.04 -0.13 -0.28 -0.27 0.01 0.04 -0.52 0.29 0.06 0.15 -0.17 0.31 0.00 -0.03 0.48
242.5 -0.10 0.01 -0.57 0.07 -0.27 0.32 -0.01 -0.24 0.11 0.35 0.04 0.07 -0.19 0.14 0.26 -0.26 -0.11 0.09 0.01 0.28
287.6 -0.21 -0.10 -0.15 0.06 -0.08 0.01 0.14 -0.17 0.16 -0.08 -0.04 -0.06 0.15 -0.48 -0.02 0.11 0.34 0.64 -0.05 -0.18
384.7 0.22 0.01 -0.33 -0.05 -0.13 -0.65 -0.05 -0.21 0.22 0.15 -0.04 -0.09 0.25 0.30 -0.10 0.32 0.08 -0.01 0.06 0.05
407.4 -0.17 -0.12 0.08 0.29 0.10 0.30 -0.04 -0.20 0.17 0.13 -0.05 -0.18 0.04 -0.02 -0.54 0.41 -0.35 -0.03 -0.06 0.24
493.4 -0.06 0.62 -0.23 -0.15 -0.11 0.28 -0.12 -0.10 -0.02 -0.38 -0.17 0.05 0.00 -0.01 -0.07 0.31 0.30 -0.23 0.02 0.06
573.4 -0.67 -0.19 -0.05 -0.18 0.03 -0.10 0.02 0.29 -0.22 0.13 0.02 0.07 -0.06 0.24 0.17 0.43 0.12 0.02 -0.02 0.06
1002.7 0.55 -0.39 -0.10 -0.29 0.00 0.30 -0.05 -0.04 -0.34 0.08 -0.05 0.05 -0.24 0.08 -0.11 0.31 0.20 0.14 -0.03 -0.06
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