Analytical Chemistry Chemical Cytometry Quantitates Superoxide

More documents

Recommendations

Info

Figure 3. Principle component score plots of the synthetic benchmark data set. Pairs-plot of the first five principal components (PCs) are shown. The separation between the inner circle (blue +-symbol) and the outer circle (red o-symbol) is obtained on PC 5. Figure 4. Relevant score plot and pseudo sample trajectories for the Synthetic benchmark data set. (a) Projection of the objects of the Synthetic benchmark data set (after the kernel transformation) in the feature space spanned by PC 1 and PC 5. (b) Pseudosample trajectories projected in the same feature space. The trajectories of the two variables representing the two circles are indicated by the numbers “1” and “2”. The five noise-variables (variables “3” to “7”) are distributed around the origin of the plot and are therefore not clearly visible. PCs show that “PC 5” can be used to visualize the class separation of the two classes. The pairs-plot for the first five PCs of the Synthetic data set is given in Figure 3. Apparently, the variance which accounts for the separation between the classes is captured by PC 5. The contribution of each original variable can be determined by projecting the kernel matrix of the corresponding pseudosamples in the feature space spanned by PC 5 and any of the other PCs and by subsequently analyzing the obtained trajectories. The pseudosample matrices were constructed by varying the intensities of the individual variables over twenty objects (uni- 7004 <strong>Analytical</strong> <strong>Chemistry</strong>, Vol. 82, No. 16, August 15, 2010 formly distributed). The application of the kernel function resulted in seven kernel matrices (one for each variable) of size (20 × 100). These matrices were then projected in the feature space obtained by PCA on the original kernel matrix K. The trajectories of the seven variables obtained by the proposed method are visualized for the space spanned by “PC 1” and “PC 5” in Figure 4b. As shown, the trajectories of the five noise-variables (variables 3-7) are along the direction of the class separation (PC 5), but are relatively small compared to the two trajectories of the variables representing the inner and outer circle (variables 1 and 2). This indicates that the noise-variables have a small contribution to the
Figure 5. Relevant score plots and pseudo sample trajectories for the Iris benchmark data set. Projection of the objects of the Iris data set after kernel transformation and PCA, to illustrate the optimal separation of the (a) Setosa, (b) Versicolor, and the (c) Virginica class (red o-symbols). The pseudosample trajectories are projected in the same feature space for the (d) Setosa, (e) Versicolor, and the (f) Virginica class. The trajectory of each variable is indicated by a different number: “1” for Sepal Length, “2” for Sepal Width, “3” for Petal Length, and “4” for Petal Width. class separation, which is in accordance with our hypothesis. As the trajectories of variable 1 and 2 have a similar length on PC 5, both variables are important for the class separation. A (linear) classification based on only one variable is therefore not possible. A visual inspection of the Synthetic data set (see the Theory and Computational Strategy section) already confirmed this conclusion. Inspection of the trajectories in the score plots of combinations of other PCs showed that the variables representing the two circles can have a relatively small loading in the particular feature space (results not shown). For example, in the space spanned by “PC 2” and “PC 4” variable 4 (representing noise) has the largest loading compared to the other variables. However, no class separation was found in this particular score-plot (see Figure 3) and therefore this feature space is not informative. Iris Benchmark Data Set. The application of the three possible one-against-all classifications resulted in cross-validated accuracies of at least 96.7% (see also SI Table 1). After PCA was applied to the three kernel matrices (for each one-against-all classifier), we searched for the PCs resulting in the optimal separation between the classes in the pairs-plots. These optimal separations are visualized in Figure 5a-c and as shown the Setosa class is completely separated from the other classes by the variance captured by PC 1. The Versicolor and Virginica class requires two principal components to capture the variance which accounts for the class separation, that is, PC 2 and PC 5 for Versicolor and PC 1 and PC 2 for the Virginica class. To determine the contribution of the original variables, pseudosample trajectories were constructed and projected in the space spanned by the corresponding PCs. These projections are visualized in Figure 5d-f and as illustrated the variables Petal Length (variable 3) and Petal Width (variable 4) are most discriminative for the classifications, confirming our hypothesis. However, the length of the trajectory of Petal Width is much shorter compared to Petal Length (for unscaled data) and is therefore less important for the classifications. To confirm these observations we have applied SVM to the Iris data set by including only Petal Length. The resulting cross-validated accuracies are given in SI Table 2. Although the accuracies for the Versicolor and Virginica class are somewhat lower compared to the results where all the variables are used (95.3% versus 96.7%), the accuracies are still comparable. If one of the other variables was chosen for the classification, that is, Sepal Width, the accuracies are much lower (
Page 1 and 2:
Anal. Chem. 2010, 82, 6745-6750 Let
Page 3 and 4:
purchased from Invitrogen-Molecular
Page 5 and 6:
Figure 3. Electropherograms of TPP-
Page 7 and 8:
Anal. Chem. 2010, 82, 6751-6755 Res
Page 9 and 10:
(pH 8.0) with cysteine and cystamin
Page 11 and 12:
Figure 4. Ion mobility mass spectra
Page 13 and 14:
GOx glucose + O298 gluconic acid +
Page 15 and 16:
coater at 2000 rpm for 20 s, and th
Page 17 and 18:
Figure 5. Comparison of cyclic volt
Page 19 and 20:
in channels with either no grooves
Page 21 and 22:
indicators of atmospheric processin
Page 23 and 24:
Figure 1. GC/MS total ion chromatog
Page 25 and 26:
Table 2. Concentrations and Stable
Page 27 and 28:
on a substrate are preferred. 20-24
Page 29 and 30:
Figure 4. SERS analysis of NAADP co
Page 31 and 32:
Anal. Chem. 2010, 82, 6775-6781 Hig
Page 33 and 34:
tion, 2 µL of proprionaldehyde wer
Page 35 and 36:
Figure 4. Analysis of 2a by HPLC-MS
Page 37 and 38:
eaction of 1a with PBH can be condu
Page 39 and 40:
Numerous references had demonstrate
Page 41 and 42:
Figure 1. TEM images of the prepare
Page 43 and 44:
Figure 4. Schematic representation
Page 45 and 46:
esult in a big SPR signal change wi
Page 47 and 48:
also reduces chemical noise, which
Page 49 and 50:
Table 1. Extraction Yields, Liquid
Page 51 and 52:
scanning of AMPP amides of the anal
Page 53 and 54:
Anal. Chem. 2010, 82, 6797-6806 δ
Page 55 and 56:
Figure 1. Schematic view of the pre
Page 57 and 58:
from the specimen and enclosed in a
Page 59 and 60:
Figure 4. (A) IRMS mass-44 chromato
Page 61 and 62:
Table 3. Ambient Measurement Result
Page 63 and 64:
Anal. Chem. 2010, 82, 6807-6813 Dir
Page 65 and 66:
Polymerase (1 U per sample). Reacti
Page 67 and 68:
of which was constant for all ampli
Page 69 and 70:
analytically useful signals at less
Page 71 and 72:
carbon black and RP-C18 for the ext
Page 73 and 74:
solution in an equal volume, and 1
Page 75 and 76:
Table 2. Concentrations and Ratios
Page 77 and 78:
Anal. Chem. 2010, 82, 6821-6829 Mac
Page 79 and 80:
mg/mL protein, followed by separati
Page 81 and 82:
Figure 2. Productivity of SEQUST an
Page 83 and 84:
Figure 4. High-resolution MS/MS spe
Page 85 and 86:
Figure 6. Characterization of PSMs
Page 87 and 88:
containing T-T mismatches. 23 Based
Page 89 and 90:
Figure 1. Extinction spectra of sol
Page 91 and 92:
Figure 3. (A) The value of Ex650 nm
Page 93 and 94:
Figure 5. Extinction spectra and co
Page 95 and 96:
wished to explore the dehydration o
Page 97 and 98:
constant medium for separation; we
Page 99 and 100:
Figure 2. Standards of (Pi)n, n ) 1
Page 101 and 102:
Figure 5. Quantitative calibration
Page 103 and 104:
Anal. Chem. 2010, 82, 6847-6853 Met
Page 105 and 106:
Figure 1. 226 Ra spectrum by liquid
Page 107 and 108:
Table 1. Counting Properties and De
Page 109 and 110:
Table 3. Analysis of 226 Ra in Sedi
Page 111 and 112:
mercial microarray scanner and fabr
Page 113 and 114:
Figure 2. Optical transmission meas
Page 115 and 116:
Figure 5. Volcano plots detailing t
Page 117 and 118:
data as well. The 41 genes in Table
Page 119 and 120:
corresponding compound if its chemi
Page 121 and 122:
Figure 1. Schematic illustration of
Page 123 and 124:
Table 1. Absolute Quantification Re
Page 125 and 126:
ment. Therefore, the long-time drea
Page 127 and 128:
Figure 1. Chemically actuated micro
Page 129 and 130:
Figure 2. Influence of a surfactant
Page 131 and 132:
Figure 5. Device to eject and mix s
Page 133 and 134:
Anal. Chem. 2010, 82, 6877-6886 Imm
Page 135 and 136:
NaCl, phosphate buffer saline (PBS)
Page 137 and 138:
Figure 2. Product ion mass spectra
Page 139 and 140:
-70 °C resolved the problem, givin
Page 141 and 142:
Table 1. Intraday Precision and Acc
Page 143 and 144:
Anal. Chem. 2010, 82, 6887-6894 Fer
Page 145 and 146:
Figure 1. Infrared spectra of (A) u
Page 147 and 148:
Figure 3. Cyclic voltammograms obta
Page 149 and 150:
Figure 6. Calculated charge from ch
Page 151 and 152:
Anal. Chem. 2010, 82, 6895-6903 Ele
Page 153 and 154:
Table 1. Chemical Structure, pKa Va
Page 155 and 156:
pH with a tilted baseline (Figure 1
Page 157 and 158:
Table 2. Linearity and Detection Li
Page 159 and 160:
ascorbic acid (AA), uric acid (UA),
Page 161 and 162:
the multielement capabilities, the
Page 163 and 164:
RESULTS AND DISCUSSION Sulfur Detec
Page 165 and 166:
Table 2. Molecular Properties and C
Page 167 and 168:
Anal. Chem. 2010, 82, 6911-6918 Dir
Page 169 and 170:
dilution and hybridization buffer.
Page 171 and 172:
solution under appropriate incubati
Page 173 and 174:
Figure 4. Standardization curve for
Page 175 and 176:
Anal. Chem. 2010, 82, 6919-6925 Ele
Page 177 and 178:
the ×10 objective, to have a large
Page 179 and 180:
Figure 2. With a suitable removal o
Page 181 and 182:
the NB signal in a much better foot
Page 183 and 184:
Scheme 1. Reactions of Selenium Rea
Page 185 and 186:
Figure 2. (a) ESI-MS spectrum showi
Page 187 and 188:
Figure 4. (a) ESI-MS spectrum showi
Page 189 and 190:
Anal. Chem. 2010, 82, 6933-6939 Dif
Page 191 and 192:
trode 28 by a finite element using
Page 193 and 194:
Figure 4. Comparison between simula
Page 195 and 196:
Figure 6. Comparison between simula
Page 197 and 198:
educed in the vicinity of double bo
Page 199 and 200:
Figure 2. Normalized product ion ab
Page 201 and 202:
Figure 4. EID (a) and IRMPD (b) of
Page 203 and 204:
Anal. Chem. 2010, 82, 6947-6957 Ide
Page 205 and 206:
Figure 1. Schematic flowchart showi
Page 207 and 208:
difference, ppm compound Table 1. I
Page 209 and 210: isoforms, its successful use, in th
Page 211 and 212: Figure 5. Extracted ion current ESI
Page 213 and 214: m/z 1172.935 was observed for Ser14
Page 215 and 216: linked products via affinity tags.
Page 217 and 218: Scheme 2. Fragmentation Mechanism o
Page 219 and 220: Figure 1. (A) ESI-LTQ-CID-MS 2 prod
Page 221 and 222: Figure 3. (A) ESI-LTQ-CID-MS 2 prod
Page 223 and 224: Figure 5. (A) MALDI-TOF/TOF product
Page 225 and 226: Anal. Chem. 2010, 82, 6969-6975 Ana
Page 227 and 228: Figure 3. Equilibrium response as a
Page 229 and 230: Figure 6. The average measured resp
Page 231 and 232: for the fill time, and we find that
Page 233 and 234: nucleotide tails. 3-5 Thus, the amo
Page 235 and 236: allow the use of higher aptamer con
Page 237 and 238: quent ligation of the aptamers afte
Page 239 and 240: Anal. Chem. 2010, 82, 6983-6990 Imp
Page 241 and 242: Figure 2. Configuration editor wind
Page 243 and 244: Figure 4. Dependencies between volu
Page 245 and 246: Since the time for liquid expulsion
Page 247 and 248: Anal. Chem. 2010, 82, 6991-6999 Int
Page 249 and 250: Figure 1. Representation of the Car
Page 251 and 252: Table 2. Capillary Electrophoresis
Page 253 and 254: Figure 4. (A) Typical raw electroph
Page 255 and 256: field. DNA profiles were delivered
Page 257 and 258: Another approach to handle this lim
Page 259: (principal components, PCs) in whic
Page 263 and 264: CONCLUSIONS In this paper we have i
Page 265 and 266: electroactive-species loaded liposo
Page 267 and 268: Figure 2. Qdot-based FLFTS response
Page 269 and 270: Figure 6. Fluorescence imaging of Q
Page 271 and 272: Anal. Chem. 2010, 82, 7015-7020 Sel
Page 273 and 274: Table 1. Effect of 1 D-LC Condition
Page 275 and 276: Figure 3. Peak-production rate vers
Page 277 and 278: Anal. Chem. 2010, 82, 7021-7026 Cel
Page 279 and 280: luciferase (T7 control vector) was
Page 281 and 282: Figure 3. Inhibitory effects of luc
Page 283 and 284: Anal. Chem. 2010, 82, 7027-7034 Pat
Page 285 and 286: Electrochemistry. Electrochemical m
Page 287 and 288: Figure 2. SEM images of Au substrat
Page 289 and 290: Table 3. Film Thickness Measurement
Page 291 and 292: Anal. Chem. 2010, 82, 7035-7043 Qua
Page 293 and 294: Figure 1. (A) FL spectra of BSPOTPE
Page 295 and 296: Figure 4. (A) Variation in the FL i
Page 297 and 298: Figure 6. (A) FL spectra of BSPOTPE
Page 299 and 300: Scheme 1. Proposed Mechanism for Fl
Page 301 and 302: one or more drawbacks including poo
Page 303 and 304: Figure 2. Free fluorophores do not
Page 305 and 306: Anal. Chem. 2010, 82, 7049-7052 Dev
Page 307 and 308: Figure 2. Comparative analysis of d
show all

Analytical Chemistry Chemical Cytometry Quantitates Superoxide

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?