LG PRODUCT INFORMATION

WT-R8571
Direct Drive Top Loading Washing Machine REPLACING WT-R857 Available: SEPTEMBER 2007

FEATURES

8.5kg Direct Drive Motor TurboDrum (i-Sensor) Fuzzy Logic 10 Washing Programs, 4 Wash Options, 3 Options Delay Start Detergent / Fabric Softner / Bleach Dispenser Auto Power-Off Child Lock 3 Star WELS Rating
* Actual product may differ slightly to that depicted for ongoing product improvement
Wash Capacity Colour Motor Type Stainless Steel Tub Fuzzy Logic Control Washing Algorithm i-Sensor Delay Start Digital Display Lint Filter Waterfalls Fabric Softener Dispenser Detergent Dispenser Bleach Dispenser Auto Restart Auto Power-Off End of Cycle Signal Child Lock Auto Levelling Legs Beeper Function

Specifications

8.5kg White Direct Drive Inverter DD 3 ~ 24 Hours Washing Programs
Fuzzy (Normal) Hand Wash Heavy Delicates Wool Eco Save Wash Only Quick Wash Rinse/Spin Spin Only

Wash Options Options

Water Temp Stain Level Water Level
10 Soak/Delay Start/Spin Hold/Custom 5 Levels 3 Levels 9 Steps 42dB 49dB 3 Star 120.4L 1 Star 919kWh/365 uses 103kWh/365 uses 685x720x1095mm 56kg 2 Years 8801031757381
WA OFFICE 18 Baile Road Canning Vale 6155 T: 08-9350-0800 F: 08-9256-1959
Washing Noise Level Spinning Noise Level Water Rating Water Consumption Energy Rating Energy Consumption

Warm Wash Cold Wash

Dimension (WxDxH) Weight Warranty EAN CODE Customer Service Helpline:
NSW HEAD OFFICE 2 Wonderland Drive Eastern Creek 2766 T: 02-8805-4000 F: 02-8805-4201 QLD OFFICE 23 Terrace Place Murarrie 4172 T: 07-3908-9000 F: 07-3399-4179 SA OFFICE 91 Transport Avenue Netley 5037 T: 08-8238-0200 F: 08-8238-0299 VIC OFFICE 3 John Deere Court Parkwest Estate Derrimut 3030 T: 03-8369-0900 F: 03-9931-0677
Note: Features and Specs are subject to change for further product development.

WT-R8571_Spec_070906.xls

Constraint-Based Modelling Of Perturbed Organisms: A ROOM for improvement Supplementary Data
Tomer Shlomi , Omer Berkman

and Eytan Ruppin

School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel Dept. of Computer Science, The Academic College of Tel-Aviv Yao, Tel-Aviv, Israel. The research of this author was done while on sabbatical at Tel Aviv University School of Medicine, Tel-Aviv University, Tel-Aviv, Israel

July 15, 2004

Flux # v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17
Genes PGI GND PFKB,PFKA1 TKTA2 TKTA1 TAL PGK ENO PYK, GLCPTS ACEE GLTA SUCA FUM MDH MAEB PPC PCK
Flux # 2,3,7,8 34,35 32,19,20,47,48,61

L.G WT 28

H.G WT 12

L.A WT 94 -47 1

L.G KO 96 -256 180

H.G KO 24

L.A KO 66
Table 1: Experimental measurements of intracellular uxes for a wild-type and knocked-out E. coli strain (taken from [1]). Each row represents a measured ux. The second column shows the names of the genes that code the enzyme that catalyze the ux. The third column species the numbers of the corresponding reactions of the in-silico model (taken from [3]). The remaining columns show the measured uxes (in percent of glucose uptake rate) for the wild-type (WT) and knocked-out (KO) strain under low-glucose (L.G), high-glucose (H.G) and low-ammonia (L.A) growth conditions. Fig 1 contains a schematic representation of the central carbon metabolism of E. coli and the uxes referred to in the paper and will be added soon. In the meanwhile it is possible to refer to Fig. 3A in [3].
Low Glucose FBA Wild-type 0.962 FBA pyk Knockout 0.078 MOMA pyk Knockout 0.535 ROOM pyk Knockout 0.603
High Glucose 0.973 0.832 0.938 0.950
Low Ammonia 0.912 0.290 0.707 0.795
Table 2: The accuracy of FBA, MOMA and ROOM ux predictions for a wildtype and mutated e. coli strain under low-glucose, high-glucose and low-ammonia growth conditions. The accuracy is computed as the Pearson correlation between the predicted ux distribution and the experimentally measured uxes. The rst row shows the accuracy of FBAs predictions for the uxes of the wild-type strain. The following rows show the accuracy of the ux predictions for the pyk knocked-out strain, obtained by each of the methods. We note that the accuracy of FBA and MOMA predictions in the glucose limited growth conditions are slightly dierent than previously reported in [3]. For the low-ammonia growth condition we nd FBAs prediction to be signicantly more accurate than previously reported in [3] as a result of alternative optimal FBA solutions.
Table 3: A list of 50 experimentally determined non-lethal yeast genes ([2]) falsely predicted lethal by MOMA.

References

[1] M. Emmerling, A. Dauner Mand Ponti, J. Fiaux, M. Hochuli, T. Szyperski, K. Wuthrich, J.E. Bailey, and U. Sauer. Metabolic ux responses to pyruvate kinase knockout in escherichia coli. J Bacteriol., 184(1):15264, 2002. [2] J. Forster, I. Famili, B.O. Palsson, and J. Nielsen. Large-scale evaluation of in silico gene deletions in saccharomyces cerevisiae. OMICS, 7(2):193202, 2003. [3] D. Segre, D. Vitkup, and GM. Church. Analysis of optimality in natural and perturbed metabolic networks. PNAS, 99(23):1511215117, 2002.