First of all, are you sure those are ice rings? They do not look typical. I think you might have salt crystals from dehydration *before* freezing. Otherwise, I think your freezing went well. Maybe try a humidity controlled environment when you freeze.
Second, I'm not so sure the bad stats come from the contaminating rings. The lattice seems to have some sort of problem, like a split lattice. You might be able to tackle this problem by increasing your spot size or skewing it's shape to compensate for the split. You need to investigate several images throughout the run to see whether and how to manipulate your spot size. Sometimes, the split lengthens the spots in the direction of the phi axis and you get lucky. But I think the phi axis might be horizontal in this picture, which makes things a little trickier. From one image, it is difficult to tell the pathology of this crystal. In principle, if you can accurately measure the most high-resolution spots visible (which appear to be about 1.9 Å, guessing from your log file) then you will have a pretty good data set, even with the contaminating rings. Personally, I'd use Denzo for this data, but I don't know what is vogue with the community right now. I still use O, so my tastes might be somewhat antiquated. James On Oct 13, 2011, at 11:12 PM, ChenTiantian wrote: > Hi there, > I am processing a dataset which has bad ice rings (as you can see in the > attach png file). > I tried both XDS and imosflm, and got similar results, it seems that adding " > EXCLUDE_RESOLUTION_RANGE" cannot get rid of the effects of the ice rings. > the following is part of the CORRECT.LP which is the second attached file, > you can find more details there. > > SUBSET OF INTENSITY DATA WITH SIGNAL/NOISE >= -3.0 AS FUNCTION OF RESOLUTION > RESOLUTION NUMBER OF REFLECTIONS COMPLETENESS R-FACTOR R-FACTOR > COMPARED I/SIGMA R-meas Rmrgd-F Anomal SigAno Nano > LIMIT OBSERVED UNIQUE POSSIBLE OF DATA observed expected > Corr > > 4.24 37152 5537 5545 99.9% 46.9% 52.7% > 37150 2.48 50.8% 19.4% -28% 0.513 5136 > 3.01 55344 9002 9840 91.5% 62.7% 65.1% > 55116 1.76 68.3% 48.1% -28% 0.520 7760 > 2.46 84636 12699 12703 100.0% 67.4% 84.7% > 84634 1.55 73.0% 54.2% -19% 0.513 12104 > 2.13 97910 14743 14987 98.4% 254.5% 199.3% > 97908 0.16 276.2% 4899.9% -23% 0.473 14037 > 1.90 110260 16846 16940 99.4% 299.2% 303.3% > 110245 0.06 325.0% -99.9% -17% 0.422 15995 > 1.74 118354 18629 18744 99.4% 1062.0% 1043.6% > 118317 -0.20 1156.4% -99.9% -13% 0.380 17414 > 1.61 122958 20193 20331 99.3% 967.5% 1571.1% > 122868 0.10 1059.7% 987.3% -2% 0.402 18348 > 1.51 125075 21554 21794 98.9% 838.9% 1355.1% > 124933 0.08 922.6% 1116.9% -1% 0.402 18977 > 1.42 72057 17042 23233 73.4% 640.8% 775.3% > 70391 0.08 732.5% 826.7% -8% 0.425 10003 > total 823746 136245 144117 94.5% 166.4% 166.7% > 821562 0.40 181.1% 296.7% -15% 0.435 119774 > > Note that I/SIGMA of each resolution shell is <2.5, so how should I do to > process the dataset properly? Any suggestion about this super ice rings? > Thanks! > > Tiantian > > -- > Shanghai Institute of Materia Medica, Chinese Academy of Sciences > Address: Room 101, 646 Songtao Road, Zhangjiang Hi-Tech Park, > Shanghai, 201203 > <csrc.png><CORRECT.LP>
