Concentration Circle Images Download Fixed

Hello everyone, I am an absolute beginner in R and I need to plot a map. There are 23 stations, each having its own Chlorophyll and POC concentration. I have already created separate maps to visualize each of them, but now I want to merge them into one map. My idea is to create a kind of pie chart, but with two 180 pieces, each representing the concentration of one parameter. I will attach a picture so you know roughly what I mean

. I know my code might be very messy, but I am also sure that you are absolute experts who can easily help me. Thank you so much for your time

Anyway, enough history. If you perform a "morphological closing" on each of your images with a disk as the structuring element, gradually increasing in radius, you will get a measure of the distribution of the sizes of blobs present in your image.

Concentration Circle Images Download

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No time to write the Python at the minute, but the graph below shows your images side-by-side with a red vertical bar to separate them. In each successive frame of the video, I increase the radius of the circular disk-shaped structuring element by 1 pixel. The first frame has radius of 1, and the final frame has a radius of 39:

I have here a special case that I have not found in the forum or on the net. I would like to visualize the accumulation of defective components on a PCB. As a starting point there is the "error category", the defective component and an assembly plan of the PCB as an image. I have already found the custom visual "Synoptic Panel by SQL BI". Unfortunately this is not quite suitable for my case as I would like to display each error that occurred as a single symbol (circle, triangle, square, etc.).

If you concentrate your mind in the middle of the circle on the black cross, then you will see that the moving gray hole will change its color to yellow. And then, as you continue to look at the cross for 10-15 seconds, all the blue circles will disappear and there will be only one yellow circle that will keep moving in the same direction.

MRI-estimated iron concentration in the 102 iron-overloaded patients (using average HIC calculated from equations 2 and 6) was 13.1 12.3 mg/g dry weight (range, 1.2-57.3 mg/g dry weight). The patients who underwent liver biopsy tended to have higher MRI HICs: 17.7 12.1 mg/g dry weight (range, 2.1-46.4 mg/g dry weight). Although not statistically different, this likely represents referral bias from physicians regarding the urgency of liver biopsy.

Figure 1 demonstrates R2* as a function of biopsied HIC. The highest iron concentration is an outlier, but linear agreement between R2* and HIC was excellent from 1.3 to 32.9 mg/g dry weight. Regression analysis yielded a correlation coefficient of 0.97, a slope of 37.4 Hz per mg/g dry weight, and a y-intercept of 23.7 Hz. Healthy controls had a mean R2* of 39.9 Hz 2.8 (SEM); this was plotted against their estimated HICs (1.17 mg/g dry weight). The R2* fit passes near this point, suggesting reasonable extrapolation to very low liver iron levels. Bland-Altman agreement of [Fe]R2* and biopsy-derived HICs is shown in Table 1. There was no significant bias, and confidence intervals were -46% to 44%, comparable with a recently published R2 methodology.18

Figure 2 demonstrates the corresponding relationship between R2 and liver iron concentration. The R2 value for the liver biopsy value of 57.8 was a significant outlier, but the R2-iron relationship appeared close to linear up to 32.9 mg/g. Linear regression between R2 and iron demonstrate a slope of 6.54 Hz per mg/g dry weight, a y-intercept of 47.4 Hz, and a correlation coefficient of 0.98. Limits of agreement between [Fe]R2-L demonstrates statistically insignificant bias (-4%, P = .48) and comparable 95% confidence intervals (-55%-46%, P = .40) to the work of St Pierre et al.18 Healthy controls had a mean R2 of 38.2 Hz 1.4 (SEM), plotted again at an estimated HIC of 1.17 mg/g dry weight. Notice that a linear R2 versus iron relationship does not extrapolate well to the healthy controls (55.1 Hz compared with 38.2 Hz, P < .001).

R2 versus HIC estimated by biopsy and by R2*. R2 versus iron (Figure 2) has been replotted to include all 102 iron overloaded patients (132 examinations). HIC was estimated by R2* (solid dots) in all examinations and by biopsy (+ signs) in 20 patients (22 examinations). Open circle represents mean R2 from 13 control subjects, plotted using an HIC value estimated from normative data (no biopsy). R2 follows a curvilinear relationship with HIC that is continuous with the mean value observed in non-iron-overloaded subjects. Solid line denotes calibration curve empirically derived by St Pierre et al.18 Agreement between this curve and estimated HIC was excellent for both biopsy-estimated iron (R = 0.97) and R2*-estimated iron (R = 0.96). All repeat MRI and biopsy examinations as well as control data were excluded from statistical calculations.

Comparison of iron concentration estimated by R2 (equation 6) and by R2* (equation 2). Regression slope is 1.01 0.02, with a correlation coefficient of 0.94. Despite this, the HIC by R2 has an 11% bias relative to values predicted by R2*, and limits of agreement are broader than for corresponding comparison with biopsy.

There is much less published data on using liver R2* to estimate liver iron. Anderson et al19 found a negative logarithmic relationship between T2* (the reciprocal of R2*) and biopsied liver iron concentration. Translated to R2* values, their data implied a near-linear rise of R2* with HIC and slope double that observed in our study. However, the confidence intervals on their regression analysis were sufficiently broad that this slope difference was not statistically significant. Their study was limited by a minimum echo time of 2.2 milliseconds, compared with 0.8 milliseconds in our study. Inappropriately long echo times severely degrade estimates of liver T2 or T2* at high iron loads.16,31 We believe that our improved agreement with liver biopsy and the high concordance between R2* and nonlinear R2 HIC estimates support our R2* versus iron calibration. R2* measurements also appear to have acceptable intermachine reproducibility,32,33 although larger-scale validations will be necessary to determine whether R2* measurements can be performed with the same machine independence recently demonstrated for R2 measurements.18

Rolling circle amplification (RCA) for generation of distinct fluorescent signals in situ relies upon the self-collapsing properties of single-stranded DNA in commonly used RCA-based methods. By introducing a cross-hybridizing DNA oligonucleotide during rolling circle amplification, we demonstrate that the fluorophore-labeled RCA products (RCPs) become smaller. The reduced size of RCPs increases the local concentration of fluorophores and as a result, the signal intensity increases together with the signal-to-noise ratio. Furthermore, we have found that RCPs sometimes tend to disintegrate and may be recorded as several RCPs, a trait that is prevented with our cross-hybridizing DNA oligonucleotide. These effects generated by compaction of RCPs improve accuracy of visual as well as automated in situ analysis for RCA based methods, such as proximity ligation assays (PLA) and padlock probes.

A rolling circle amplification product (RCP) is a long repetitive single-stranded amplicon consisting of hundreds of reverse complementary elements of a circular template, lined up in a single molecule1. These RCPs can be probed with fluorophore labeled oligonucleotides, i.e. detection oligonucleotides, to visualize single molecules in situ by standard fluorescence microscopy. Several methods rely on rolling circle amplification (RCA) to generate signals in situ, such as immuno-RCA (iRCA)2, Proximity Ligation Assay (PLA)3 and padlock probes4,5. Immuno-RCA is used to detect a single protein by an antibody conjugated to an oligonucleotide. To this a circular DNA template can be hybridized and then amplified by the enzyme phi29 polymerase, using the oligonucleotide conjugated to the antibody as a primer. This will create a RCP, covalently linked to the antibody. In PLA, two antibodies are conjugated with two different oligonucleotides (proximity probes). If the proximity probes bind to their targets in close proximity they will act as a hybridization/ligation template for two additional oligonucleotides and a circular DNA template can be created by ligation, which can be amplified using one of the proximity probes as a primer for RCA. Padlock probes are used to detect DNA or mRNA and are sensitive enough to detect single nucleotide variations. The padlock probe consists of an oligonucleotide with its ends complementary to its target sequence (DNA or cDNA). Upon hybridization of the padlock to the target a circular template is created by ligation of the juxtaposed two ends of the padlock probe. The DNA, or cDNA sequence will be used as a primer for RCA.

An obstacle with RCA-based methods is that with an increased concentration of RCPs, the RCPs start to coalesce and individual products cannot be discerned7. Therefore, it is desirable to develop RCA based assays that can generate signals smaller in size for an increased dynamic range.

Schematic cartoon and visualisation of regular vs. compacted RCPs. The oligonucleotides from top to bottom in (a) are the detection oligonucleotide, the compaction oligonucleotide, the long circularization oligonucleotide and the short circularization oligonucleotide. The detection oligonucleotide (Table 1, oligonucleotide A) has the same sequence as a part of the long circularization oligonucleotide (cyan), nucleotides preventing degradation and priming (grey) by the polymerase and a fluorophore (star). The compaction oligonucleotide (Table 1, oligonucleotide B) has two copies of the same sequence also found in the long circularization oligonucleotide (magenta), a spacer sequence (black) and nucleotides preventing degradation and priming (grey). Apart from the already mentioned sequences, the long circularization oligonucleotide (Table 1, oligonucleotide C) also has a spacer sequence (black) and parts hybridizing to the PLA probes (yellow). The short circularization oligonucleotide (oligonucleotide D in Table 1) has a sequence complementary to each PLA probe (not colored), spaced apart by a short sequence (not colored). The circularization oligonucleotides are ligated together in a separate step preceding the RCA reaction, resulting in (b). In regular RCA, the fluorophore labelled detection oligonucleotide is the only added oligonucleotide and the resulting RCP is depicted in (c). Adding also the compaction oligonucleotide to the RCA reaction results in a less dispersed RCA product (d). The bottom images were acquired with a 3D Structured Illumination Microscope and depict RCPs generated from two different circular templates with the dissimilarity of two different sequences for detection oligonucleotide hybridization. One of the detection oligonucleotides labelled with Alexa488 (green) and the other with Alexa642 (red). The images show RCPs with (e) and without (f) compaction oligonucleotide. 2351a5e196