We are always working hard on creating driver for your vinyl cutter(s) and we are proud to say that Easy Cut Studio is now supported with more than 700 vinyl cutters and cutting plotters. If you don't find your vinyl cutter in this list, contact our tech support to check if our software is compatible with your vinyl cutter/plotter.
To operate safely and reliability, businesses depend on actionable information. Bently Nevada's hardware and software offerings, enabled by analytic insights and managed services, help organizations improve asset performance while effectively managing risk and reducing costs.
To get the latest driver, including Windows 11 drivers, you can choose from the above list of most popular GCC downloads. Click the "Download driver" button next to the matching model name. After you complete your download, move on to Step 2.
If you are having trouble installing your driver, you should use the Driver Update Utility for GCC. It is a software utility that automatically finds, downloads and installs the right driver for your system. You can even backup your drivers before making any changes, and revert back in case there were any problems. You can safely update all of your drivers in just a few clicks. Once you download and run the utility, it will scan for out-of-date or missing drivers:
For most markers, the parental phase in the Kanota and Ogle mapping parents was known, and +/- marker scores were converted to the scoring convention used by various software packages. However, the parental phase for 98 markers with segregating polymorphisms was unknown. This was either because marker alleles were not called in either parent (27 markers), or because both parents showed the same genotype (71 markers). When sequences of the corresponding clones belonged to contigs that contained markers with known phase, the phase was inferred from those markers. However, when the phase was completely unknown, two separate copies of the marker were scored in opposite phase and appended with the suffix "_rp" or "_rp2". One copy was then identified as being in the correct phase based on map placement, but a few markers with these extensions remain in the additional files when the correct phase could not be inferred with certainty. Most of the markers with identical scores in Kanota and Ogle had the 'plus' allele in both parents. It was later noted that the 'Ogle_1040' entry (a reselection of Ogle, typed in the diversity analysis but not in the marker analysis) had scores that agreed better with the segregation pattern in the KxO progenies, confirming that this entry is more closely related to the original Ogle parent used in the KxO cross. Furthermore, a large proportion of the markers for which Ogle and Ogle_1040 had different scores were later mapped to a region on KxO linkage group 4_12_13 between cM position 139 and 159. Thus, the two Ogle sister lines could be useful in future studies to elucidate effects of QTL within this region.
Although the major structure of the new map agrees substantially with that of previous maps (Additional file 6), the current map provides new evidence for joining previous linkage group fragments, as well as for revising the order of some linkage groups (Table 2). The most substantial difference in the newly-presented map is that the linkage group previously published as "KO_3+38" has been deliberately broken into several sections to discover linkage group fragments that may belong to translocated chromosome arms. Chromosomal interchanges among hexaploid oat genotypes are well known , and it has been confirmed that most spring oat genotypes (including Ogle) contain a reciprocal intergenomic translocation involving chromosomes 7C and 17, whereas non-translocated versions have been found in many North American red-oat types (including Kanota) . The postulated effect of this translocation on the KxO map is to suppress recombination near the interchange breakpoint due to the formation of a quadrivalent meiotic structure. Non-lethal meiotic interchanges on the arms of this quadrivalent structure can produce recombination events along the four separate linkage group arms, resulting in four linear series of recombination events, all with statistical linkage to a single recombination-suppressed breakpoint. This would properly be recorded as an 'X'-shaped linkage map; however, software written to test this  has not produced conclusive results in KxO. Because of the strong likelihood that more than two linkage groups are associated with this breakpoint, we deemed it useful to deliberately isolate markers in the breakpoint region as a separate linkage group to allow generation of multiple additional groups. This strategy appears to have had the intended effect, because there are now five separate linkage groups (including the breakpoint region) formed from markers previously assigned to linkage group 3+38, as well as the markers previously found in group KO_1 (considered to be a homoeologous group by Wight et al. ). Although this is a potentially useful dissection of meiotic linkage groups, it should not be considered as being conclusive regarding the physical arrangement of markers on the translocated arms. Further mapping of these markers in populations lacking the translocation difference should be conducted to resolve this issue.
Slides were scanned using a Tecan LS300 (Grödig, Salzburg, Austria) confocal laser scanner. The TIF images derived from the slide scanning were analysed using DArTsoft version 7.3 (Cayla et al. in preparation), a dedicated software package developed at DArT P/L which is available to DArT network members . DArTsoft was used to automatically analyse batches of up to 96 slides to identify and score polymorphic markers. Briefly, the relative hybridisation intensity of each clone on each slide was determined by dividing the hybridisation signal in the target channel (genomic representation) by the hybridisation signal in the reference channel (polylinker). Clones with variable relative hybridisation intensity across slides were subjected to fuzzy k-means clustering to convert relative hybridisation intensities into binary scores (presence vs. absence). Clones that did not fit an expected bimodal (two-cluster) distribution were discarded from further analysis. Entries from the diversity panel and from the KxO mapping population were screened separately on the three discovery arrays. Because not all of the diversity entries were available at each stage of screening, and because technical difficulties resulted in some lines being omitted, the actual composition of entries screened on each array was slightly different (see Additional file 2). Standard methods of marker discovery were deployed using a combination of parameters automatically extracted from the array data using the DArTsoft program: (1) marker quality (Q), which measures between-cluster variance as a percentage of total variance in fluorescent signal distribution among tested samples, (2) marker call rate (percentage of effective scores), and (3) Polymorphism Information Content (PIC). The markers reported in this paper were selected with Q >73, call rate >80%, and PIC >0.1.
Due to the large number of markers available for mapping, the relatively small size of the KxO population, the large size of the oat genome, and some cytogenetic abnormalities in the KxO cross , it was not feasible to construct a map using a single mapping procedure. To achieve a robust result, two different mapping programs were used: EasyMap (Wenzl, P., unpublished) and JoinMap V.3 . Results from these programs were compared to previous maps generated using an additional two programs (GMendel  and Mapmaker ). Short descriptions of the algorithms employed by these programs are provided in the next paragraph. The data set used in this work is shown in Additional file 5. The first step involved de novo map construction using EasyMap. This step was performed by authors who were not previously familiar with oat linkage maps; thus, it provided a good validation of previous work. The second step involved matching the new map with previous versions constructed using GMendel  and Mapmaker  based on positions and groupings of the common framework markers. Where one or the other map suggested a merging of linkage groups, or if groupings conflicted, the markers in question were re-tested with JoinMap v.3  using a small data set that included only those markers. If a single group could be formed at LOD 5, or if a group could be formed at a lower LOD that was compatible with aneuploid assignment , then the JoinMap version was accepted. All groups were further tested using JoinMap to re-estimate marker order within the group, and the three different map versions were compared using the software C2Maps (an enhancement of M5 , available from the corresponding author). Either the JoinMap or the EasyMap version of the ordering was accepted, depending on which was closest to the previously published order of framework markers within a given linkage group.
Where possible, pedigrees of varieties in the germplasm panel were obtained from the literature or through correspondence with colleagues. The resulting information was incorporated into the online relational database called 'Pedigrees of Oat Lines (POOL)' [43, 44]. This database allows querying of extended pedigrees when varieties share common intermediate parents, and provides a convenient keyword-search for names and synonyms of varieties. Once the pedigrees were incorporated into POOL, a complete matrix of co-ancestry coefficients (K) among varieties was computed using an updated version of the software package KIN . All varieties, landraces, and intermediate breeding lines in the pedigrees were assumed to be 100% homozygous and homogeneous for the purpose of these computations. Values of D were used to construct an UPGMA-based dendrogram using the same methods described for the marker-based diversity analysis (above). 2b1af7f3a8