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Groupe de #TwinsProd

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SewArt Embroidery Digitizer – S & S Computing

Related to this, you should regroup the same colors in two situations: You got a single needle machine or your the number of objects is high (a few hundreds). For the moment, Ink/Stitch cannot fix adjacent threads, so you have to make sure that the source SVG code is color sorted.

Sew Art 1.8.2 Serial Number

Completed Lesson 1 with no problems.Completed Lesson 2 but I have a problem. The IR controller does not work. I have used the troubleshooterbut got no results on the serial printer. Is there any other way to test the IR receiver and IR transmitter controller?

With almost 650,000 units built, the jeep constituted a quarter of the total U.S. non-combat motor vehicles produced during the war,[nb 5] or almost two-thirds of the 988,000 light 4WD vehicles produced, when counted together with the Dodge WC series. The jeep massively outproduced its primary Axis counterpart, Nazi Germany's Volkswagen Kübelwagen, which only had a production total of 50,000 units.[12] Large numbers of jeeps were provided to U.S. allies through Lend-Lease.

Meanwhile, in Asia and the Pacific, Japan had invaded Manchuria in 1931, and was at war with China from 1937. Its Imperial Army used a small, three-man crew, four-wheel drive car for reconnaissance and troop movements, the Kurogane Type 95, produced in limited numbers from 1936.

The jeep's primary command and reconnaissance roles of course necessitated fitting many kinds of tactical communication equipment. The first standard production fitting was for the SCR-193 radio, placed on either side in the rear of a jeep, on top of the rear wheel well. For proper reception, this included radio interference suppression shielding, so indicated by a suffix 'S' on the jeep's hood registration number. In 1943/1944, the Army shifted to FM radios, and new fittings were developed for those. At least fourteen Signal Corps Radio set fittings were standardized, including for the SCR-187, SCR-284, SCR-499, SCR-506, SCR-508, SCR-510, SCR-522, SCR-528, SCR-542, SCR-608, SCR-610, SCR-619, SCR-628, SCR-694, SCR-808, SCR-828, and VRC-l.[75]

The jeep being too light to mount substantial guns, it was more suited later in the war, as a platform for rocket artillery, that did not have the enormous recoil as conventional tube artillery. The California Institute of Technology developed two different 4.5-inch jeep-based rocket launcher systems for the U.S. Navy. Several other initiatives all used 4.5-inch rockets and tubes. Testing was also done by both U.S. Army and Marine Corps, but none of the jeep-mounted rocket launchers were built in any significant number because it was more efficient to use larger trucks that could carry more rockets. The Soviet Red Army deployed twelve units fitted with 12-rail M-8 82mm rocket launchers in the bed of a jeep, from December 1944 in the Carpathian Mountains.[85]

We performed power simulations to estimate the number of MM lines required to detect QTL of a certain effect size. We simulated 20 QTL with effects following a geometric series with the same principle that drove simulations on the NAM population [25], under heritability of either 0.4 (Fig. 6a) or 0.7 (Fig. 6b). We found that power increased with increasing sample size (from 100 to 500), and increasing effect size. Note that mapping resolution also rises with sample size as more lines increase the number of observed recombination events. At heritability 0.7, the use of 500 samples permits the detection of QTL explaining 8% phenotypic variance with >90% power (Additional file 11: Table S5). QTL mapping with 100 MM lines is far from this power, yet panels as small as 300 already mirror the QTL mapping with 500 MM lines. Three hundred MM lines detect QTL accounting 12% of variance with a power of 82%. Sample sizes of 500 and 400 approach a plateau of high power with QTL explaining about 10% of variance. The same simulations were plotted with power as a function of sample size alone to allow a graphical comparison with the NAM power report [25] (Additional file 12: Figure S7). QTL were sorted in effect size quartiles to survey the MM mapping power with high-effect and low-effect QTL separately. Using 500 MM lines with heritability 0.7 permits to detect more than 40% of the 20 simulated QTL. In this scenario, the five QTL having the highest effect are detected with a power close to 90%, whilst QTL with a lower effect are hardly identified.

The MM brings together high genetic diversity and low population structure in elevated MAF, all positive characteristics for QTL mapping [4]. This is the result of a breeding scheme that largely avoids directional selection during the production of the RIL. During production, the population was kept as large as possible, both to avoid genetic drift and to gather a large number of recombination events without the need of additional intermating generations. Although high level of sequence variation between the lines might inflate heterozygosity by inefficient hybridization on the chip, we did not observe significant departure from the values expected in the MM lines. We did not observe higher heterozygosity in centromeric regions [40], indicating that it was not selected during MM breeding [9]. The low observed pericentromeric heterozygosity might also have resulted from low marker resolution on the genotyping array in that region (Additional file 4: Figure S2). Keeping subfamilies separated allowed us to track the origin of each line once the final population was produced. The genetic distance between lines is evenly distributed (Fig. 3), which implies that subsets of the MM panel might be selected for specific research purposes without losing the general features of the population. The divergence of Mo17 from the other lines may be due to marker selection bias on the genotyping array, as B73 and Mo17 were used to select many of the SNPs on the genotyping array [20, 41]. This might also explain the apparent similarity between B96, CML91, and F7.

After haplotype reconstruction, the MM lines showed an average of 80.9 recombination events (Additional file 9: Figure S6). The current MM genetic map is derived from the maize IBM population, whose two founders are also included in the MM. Its length (1,996 cM) can be used as a fair approximation to calculate the number of expected recombination events in the MM lines. In RILs, we sample one set of chromosomes and thus we count one round of recombinations per generation. We would observe no recombinations in G0. Assuming a maize genome length of 19.96 Morgans, we would observe 19.96 recombinations in G1 generation and likewise in G2 and in the first G3 selfing. One additional fully effective round of recombinations results from the single seed descent because of heterozygosity halving at each generation, bringing the total to 19.96 4 = 79.8. The observed recombinations in the MM (80.9) are thus remarkably close to the expectancy. Based on this count, we would expect more than 130,000 recombinations in the full population of 1,636 MM lines. Genome reconstruction, however, might be further improved by using sequence-based molecular markers. Work in other MpCD confirms that the genotyping approach may affect genome reconstruction efficacy [12], notably in the presence of wide regions identical by state (IBS). Deviations in the estimation of founder contributions in MM (Fig. 5) are likely due to the inability of the current genotyping method to distinguish between the founder lines. A632 and B73 are the most similar (Fig. 3a and Additional file 9: Figure S6), and this is expected since A632 and B73 were independently derived from the same source [43]. In the future, we envision low density sequencing approaches on the whole MM population that should allow us a finer reconstruction of RIL haplotypes by distinguishing between pairs of strains in IBS regions.

The MM population represents a new and powerful tool for the fine dissection of quantitative traits in maize. Multi-parent crosses are the future of complex trait genetics [4]: here we have shown that the MM population contains roughly equal proportions of the founder genomes, that the genetic distance between the lines is evenly distributed and that the LD decays sharply. QTL mapping panels suffer a tradeoff between mapping power and definition. Faster LD decay increases the number of independently tested markers, which reduces power. However, the high MAF in the MM rescues the power to map rare variants. Simulation results showed that relatively small sample sizes could achieve high power for QTL detection (Fig. 6 and Additional file 12: Figure S7). Such results might be used as a guideline for choosing appropriate sample sizes for future studies. As the MM panel contains no population structure (Fig. 3b), any MM subsample may be used for QTL mapping. In contrast, fragmented, star-like designs, such as the maize NAM, require a higher number of samples to achieve effective QTL mapping.

The simultaneous simulation of several QTL reflects the genetic architecture of complex traits in maize, which is expected to be contributed by manifold QTL with medium to small effects [31]. We simulated 20 QTL on the MM to permit a comparison with the NAM panel, showing how a relative small number of MM lines can achieve high mapping power. One-third of the complete MM population confidently detects QTL with mid-to-high simulated effects, as our field test further showed. NAM simulations are not reported for less than 1,000 lines, yet a preliminary comparison with 500 MM lines provides interesting insights. In fact, under the same conditions of 20 simulated QTL with h 2 = 0.7, 1,000 phenotyped NAM lines have an average mapping power of around 50%, while an MM panel half that size reaches a 41% power (500 lines; Additional file 9: Figure S6b). In the case of lower heritability (h 2 =0.4; Additional file 12: Figure S7a), the MM power at 500 lines (22.1%) is also similar as that of twice the number of NAM lines. False positives appear higher in the MM than in the NAM, especially when exceedingly small number of lines are considered. The FDR trend in the MM however becomes rapidly lower, especially in the case of h 2 = 0.7. Notably, the MM shows top-quartile QTL detection power higher than the NAM in both heritability conditions. This suggests that high effect QTL can be detected with small sample sizes. Finally, it is worth noting that each individual RIL of the MM encompasses more recombination events than a NAM RIL, increasing mapping definition when considering mapping panels of equal size. Once the genotyping of the whole MAGIC maize population is achieved, the power of the MM will be assessed in full.

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