My take on the app slightly biased, I admit is that HTH took their time and built it well. Everything is easy to find and use. Convenient features such as auto-saving recently viewed items, and manual-saving for repeat viewing are built-in.
All of the information is downloaded onto your iPhone or iPod touch so that it works even without an Internet connection. Of course, using the mapping features and Google Maps for walking or driving directions as well as sending an appointment request require a connection. I noticed a couple of things that will get fixed in the next update. QTL for yield-related traits in alfalfa have been reported in previous studies [ 5 , 17 , 27 ].
The use of single-dose alleles SDAs for genetic mapping is feasible in tetraploid species. Adhikari et al. This method is a pseudo-testcross strategy, which uses the simplex markers AAAB x BBBB of an F 1 population for autotetraploid genetic linkage map construction using diploid software like JoinMap [ 28 ]. The pseudo-testcross strategy allows us to use thousands of SDA markers to construct linkage map followed by QTL mapping.
Among 21 QTL associated with yield and yield-related traits, several QTL were co-located among the yield-related traits.
This indicated that phenotyping in multiple environments and adjusted BLUP is a useful way to control environmental variation.
These QTL may play a major role in controlling yield. Additional QTL such as qyield-2 and qheight-2 were co-located with other traits, such as fall dormancy QTL, which may suggest pleiotropic effects of the genes.
We are the first to map QTL for shoot diameter and branch number in alfalfa. Although the heritability was not very high Several QTL for shoot diameter and branch number were co-located with QTL for yield and plant height: qyield-1 was co-located with qbranch-2 ; qyield-2 with qbranch-3 ; and qheight-5 with qdiameter-6 Figs. Furthermore, most shoot diameter and branch number QTL identified in the present study were co-located with shoot dry weight, crown dry weight, or root dry weight QTL [ 17 ].
These QTL may have an indirect effect in controlling yield traits [ 29 ]. Overlapping QTL have been reported using different populations with different genetic backgrounds in different environments [ 3 , 17 , 19 , 20 ]. Furthermore, in the present analysis, we were able to narrow down the QTL interval with high PVE qyield-1 and qbranch-2 to 1.
Our results showed that RAD-seq is an appropriate method for generating genetic markers that can be used to construct linkage maps in alfalfa. The QTL detected in this study will help us to understand the genetic basis of yield-related traits. However, these QTL may be not robust in different populations or environments and thus must be validated in breeding populations in future studies.
With further investigation, markers closely linked to the major QTL can be used for marker-assisted selection to improve yield in alfalfa. P1 paternal parent; Cangzhou and P2 maternal parent; Zhongmu No. They were crossed to generate an F 1 population consisting of progeny lines. Clones were propagated from individual plants by stem cuttings. During the early branching stage in , the cloned plants were moved from propagation flats to the field of the CAAS research station in Langfang, Hebei Province F 1 and parent individuals were also transplanted to establish a field trial in Tongzhou, Beijing The field trial was carried out using a randomized complete block design RCBD with three replications at each of the two field sites.
Every replication had one clone plant for every individual. The individuals were not similar with each other after transplanting. Alfalfa is a perennial plant and is harvested by cutting; its rapid regrowth after cutting makes alfalfa a high yielding forage crop. Weeds were removed manually and there was no cover crop in the field. Phenotypic data were collected at two locations in multiple years.
Yield was measured after harvested plants were dried in a forced-air dryer. Plant height was measured based on the tallest stem at the date of harvesting. Branch number the number of all stems was counted at the same time. Diameters of five randomly selected shoots were measured at the shoot bottom after harvesting and the mean value was calculated. Data of yield-related traits from the first harvest was used for our analysis.
All factors were random effects except for the grand mean. Samples were pooled together and randomly sheared ultrasonically. The Tassel 3. We initially used the Medicago truncatula genome Mt4. Other parameters were set as default. In total, SSR markers were also used for genotyping.
Polymerase chain reaction PCR was based on the method of Diwan et al. Single-dose alleles SDAs of SNP markers with a ratio of less than among F 1 progenies were used to construct a genetic linkage map as described by Li et al.
SSR markers with a ratio of in F 1 progenies were used to construct genetic linkage maps. Second, for each group, markers were clustered using default parameters.
Linkage group numbers were assigned based on SSRs with known chromosome locations. We thank Wenshan Guo and Fengqi Liu for their technical help in the field work and data collection. The funding body played no role in the design of the study, the collection, analysis, and interpretation of data and the writing of the manuscript. All authors read and approved the final manuscript.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. We illustrated routes between two locations with a straight line, and we explored slope by comparing the steepness of lines for different routes.
Students were able to practice what they learned by creating their own map, in which they needed to be able to write equations from the routes they drew as straight lines. The map they created would serve to assess their ability to translate lines to equations. Once they traded maps with another group, I could assess their ability to translate equations back to lines in order to create a route and eventually uncover the treasure.
In total, each group wrote ten equations to direct treasure hunters from one point to another. Students chose the best method to write the equation for each line.
Some used the line they drew to find the slope by tracing the rise and run with their finger, and then moved on to find the y-intercept by noticing where the line crossed the Y-axis. In order to express only a particular line segment that was their route, students bound the line using the domain and range. Once the linear equations, domains, and ranges for each route were found, students recorded the information onto the index cards that would become the clues for their treasure hunt.
One of the challenges that students encountered in crafting their maps was that not all lines crossed the y-axis in a place that was visible on the blueprint of the school. This initially frustrated some students, many of whom had really only paid attention to the slope-intercept approach for writing equations.
And zoom: 1 makes the default zoom to whole world. For example only one instance of world gets shown and bottom of the map will be the bottom of Africa. So subsequently world map will not repeat. To be precise making our map bound like this:. This is what I have got so far:.
The 'picture' of the whole world has a constant size at a given zoom level. To fit the whole world without repeating it you need to have a map div that is smaller than the picture of the whole world at this zoom level, otherwise it won't work. It also resizes the map to fit the whole world when you zoom in a silly thing to do usually, but here it just shows how this works.
Google maps has a series of zoom levels - each one getting one step closer. You set your map height and width in pixels, and then the google maps api will load the appropriate map tiles to fill the provided window. In effect, it doesn't allow you to stretch the map, and I believe at zoom 1, the google map of the world would be px by px.
Also, if you're dropping pins dynamically in a webpage, you can use the api to add markers directly by their latitude and longitude. If you're working with an offline static image, then first, you should look at using the open source openstreetmaps data, as you'll may need a further license with Google. For openstreetmaps, you might look at using offline tool such as TileMill or mapnik for placing the markers?
In any case - read up on projections - this will explain how latitude and longitude coordinates will correspond to the pixel position on the image.
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