The use of genetic information to predict the relative maturity of soybeans

Research suggests that in North America, soybean Relative Maturity (RM) is controlled by a minimum of eight genetic loci labeled E loci. The amount of variation explained by these genes would suggest that accurate predictions for RM could be obtained using prediction models that only include allele effects for markers located near the major E genes. Having the ability to accurately predict the RM of a segregating breeding line using genetic information has the potential to positively impact both the rate of genetic gain and cost per unit of genetic gain within a breeding program by enabling; 1) prediction of RM in segregating progeny from crosses between parents with large differences in RM; 2) selection of segregating lines with appropriate RMs in non-adapted off season nurseries; 3) increased selection intensities of segregating lines assigned to field trials; and 4) cost reduction of replicated field trials. The objectives of this research then was to; 1) compare the accuracy of RM prediction using genome wide markers versus using prediction models containing only molecular markers significantly associated with RM; 2) validate that prediction accuracies were maintained when predictions were made for segregating lines not only having distant relationships to those in the original training dataset, but also developed and grown outside of the years of the segregating lines in the original training dataset; and 3) evaluate if the prediction accuracies and associated genotyping costs support wide scale RM prediction within a soybean cultivar development program. In effort to determine if the RM of a segregating soybean breeding line could be predicted using genetic information, we developed a training dataset that consisted of 1,244 F4 derived advanced stage segregating soybean lines having known RMs ranging from RM 1.3 to 8.0 that were genotyped with 1,817 genome wide single nucleotide polymorphism (SNP) markers. The segregating lines were selected from multiple families that were the result of hundreds of breeding crosses made over multiple years in a soybean cultivar development program. The data were utilized to determine allele effects for four prediction models, two models that represented traditional Marker Assisted Selection (MAS) approaches using only markers associated with known E genes or within regions of the genome thought to influence RM (specific E-gene and expanded E-gene) and two Genomic Prediction (GP) models with distinct marker densities (full GP model and reduced GP model). The GP and expanded E-gene prediction models evaluated in the study produced an average across RM prediction accuracy from 0.93 to 0.94 while the E-gene specific model prediction accuracy was 0.81. The results indicated that the E genes identified in the literature were highly predictive of RM, the greatest prediction accuracies however were obtained through the use of whole genome marker panels. While the results from the initial research were promising, additional research was required to determine if the prediction accuracies could be maintained when predictions were made on segregating lines outside of the years of those contained within the original training dataset. In an attempt to strengthen the prediction accuracies obtained for the early and late maturities, the original training dataset was expanded to include a total of 2,194 segregating lines that were selected from replicated field trials in 2009-2013 having validated RM phenotypes that ranged from RM 0.0 to 8.0. All of the 2,194 segregating lines within the updated training dataset had previously been genotyped using 1,118 genome wide SNP markers. Since it was identified in the preliminary research that prediction accuracies were highest when whole genome marker panels were used, only a full GP model using allele effect estimates for all 1,118 SNP markers was evaluated in this study. The 1,118 SNP marker GP model successfully predicted the RM’s of 1,854 segregating lines in 2014 and 1,465 segregating lines in 2015. The estimated correlation between predicted RM (RMp) and validated RM (RMv) for all segregating lines was 0.95 with an average difference between RMp and RMv of 4 days. Prediction accuracies were again the lowest for segregating lines with RMv earlier than 1.0 and later than 5.0 which we feel was still likely the result of a small number of segregating lines in the training set for those RM groups. Alternative metrics including the frequency of RMp within 0.5 of RMv, f(|RMp-RMv|≤ 0.5) and the frequency of RMp within 0.25 of RMv, f(|RMp-RMv|≤0.25) were developed that indicated that across years, 66% of the segregating lines had RMp that were within 5 days of their RMv and 39% of the segregating lines had RMp that were within 2.5 days or their RMv. The f(|RMp-RMv|≤ 0.5) and f(|RMp-RMv|≤0.25) improved to 73% and 46% respectively when only segregating lines with RMv that ranged from 1.0 – 5.9 were evaluated. While the results from this second round of research proved that genetic information could be used to predict the RM of segregating lines with relatively high accuracy across the maturity groups grown within NA, additional analysis was required to determine if wide scale implementation could be justified within a breeding program. In effort to determine if the prediction accuracies and genotyping costs associated with predicting the RM of segregating lines using genetic information could be justified for wide scale implementation within a breeding program, we evaluated the program wide implementation of RM prediction using basic principles of Operations Research (OR). A simple Microsoft Excel based tool termed the Genomic Prediction Evaluation Tool (GPE tool) was built that allowed all possible cultivar development scenarios that exist within the Iowa State University soybean breeding program to be evaluated to determine both Total Program Cost (TPC) and Relative Breeding Design Efficiency (RBDE). Optimal breeding designs were those designs that both maximized RBDE while minimizing TPC. Two analysis were conducted using the GPE tool. The first analysis (analysis 1) determined the total number of years to reach the final year of replicated field trials as the number of years from the initiation of crossing to the final year of field trials. The second analysis (analysis 2) added a year to the total number of years from crossing to the final year of field trials for those designs that utilized a North American summer crossing block, thus decreasing associated RBDE. Of the optimal breeding designs identified from both analysis, no design was identified that recommended the use of RM prediction to support the cultivar development process, the associated cost of implementation was simply too high. Slight modifications to the current version of the GPE tool should allow the ISU breeding program to identify more efficient breeding designs as compared to the current design that has been implemented to date. The GPE in its current version sets the foundation to build a tool that will provide soybean breeders the ability to appropriately evaluate the potential wide scale implementation of GS to predict complex phenotypes in support of soybean variety development

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

The inter‐annual variability of southerly low‐level jets in North America

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135612/1/joc4708_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135612/2/joc4708.pd

Reassessing the Fighting Performance of Conscript Soldiers During the Malvinas/Falklands War (1982)

While the idea is controversial, it is quite possible that, at least under certain circumstances, the fighting effectiveness of a conscript army can equal that of a professional army. For any army, fighting effectiveness is not only influenced by the degree of psychological cohesion among soldiers and officers, but also by the organizational culture of each particular service unit towards the preparation for war and the waging of the conflict itself. The Malvinas (Falklands) War of 1982 demonstrates this very well. In this war, two different types of armies confronted one another: the British army, a professional and all volunteer force, and the Argentine army constituted principally of conscripted soldiers. In this regard, some analysts assert that the British concept was vindicated when a force of British professional soldiers defeated an opposing Argentine force of draftees twice as numerous. Analysts in general have rated the capabilities of the Argentine land forces as poor, although there were exceptions and some units performed very well. These cases deserve to be studied. Notably, the most effective Argentine effort came from some small Army units and one Navy unit, the 5th Marine Battalion. For these units, two primary reasons account for the differences in fighting performance. First, small Army groups fought well because there was cohesion among their components, conscripts, noncommissioned officers, and junior officers, especially by the attitude of the latter. Secondly, in the case of the Marine battalion, its performance was the product not only of good training, but also of the different institutional approach to waging war that the Argentine Navy employed. These, in turn, improved cohesion. By focusing upon these units and their effectiveness, a rather new picture of the Malvinas War comes to light that differs quite substantially from those drawn in the immediate aftermath of the war itself. It should also make us rethink the lessons of the war, including those that surround the professionals versus conscripts controversy

Validar a guerra: a construção do regime de Expertise estratégica

This article is intended to contribute to the interpretative analysis of war. For that purpose, it investigates how some apparatuses located in strategic thinking help to make modern war a social practice considered both technically feasible and, at the same time, legitimate for soldiers. In so doing, it makes use of two different but closely related theoretical fields, pragmatic sociology (finding inspiration in the work of scholars such as Luc Boltanski, Nicolas Dodier and Francis Chateauraynaud), and the sociology of scientific knowledge (based mostly on the work of Bruno Latour). On the one hand, the sociology of scientific knowledge has developed a productive questioning of the construction of scientific facts that is particularly relevant to the present research. On the other hand, pragmatic sociology generates a compatible framework able to describe collective actions. The combination of both approaches allows the description of the formation of a strategic expertise regime that supports the technical legitimacy of the use of military force. Together, the sociology of scientific knowledge and pragmatic sociology bring a particularly relevant perspective to research pertaining to war.info:eu-repo/semantics/publishe

The use of genetic information to predict the relative maturity of soybeans

Research suggests that in North America, soybean Relative Maturity (RM) is controlled by a minimum of eight genetic loci labeled E loci. The amount of variation explained by these genes would suggest that accurate predictions for RM could be obtained using prediction models that only include allele effects for markers located near the major E genes. Having the ability to accurately predict the RM of a segregating breeding line using genetic information has the potential to positively impact both the rate of genetic gain and cost per unit of genetic gain within a breeding program by enabling; 1) prediction of RM in segregating progeny from crosses between parents with large differences in RM; 2) selection of segregating lines with appropriate RMs in non-adapted off season nurseries; 3) increased selection intensities of segregating lines assigned to field trials; and 4) cost reduction of replicated field trials. The objectives of this research then was to; 1) compare the accuracy of RM prediction using genome wide markers versus using prediction models containing only molecular markers significantly associated with RM; 2) validate that prediction accuracies were maintained when predictions were made for segregating lines not only having distant relationships to those in the original training dataset, but also developed and grown outside of the years of the segregating lines in the original training dataset; and 3) evaluate if the prediction accuracies and associated genotyping costs support wide scale RM prediction within a soybean cultivar development program. In effort to determine if the RM of a segregating soybean breeding line could be predicted using genetic information, we developed a training dataset that consisted of 1,244 F4 derived advanced stage segregating soybean lines having known RMs ranging from RM 1.3 to 8.0 that were genotyped with 1,817 genome wide single nucleotide polymorphism (SNP) markers. The segregating lines were selected from multiple families that were the result of hundreds of breeding crosses made over multiple years in a soybean cultivar development program. The data were utilized to determine allele effects for four prediction models, two models that represented traditional Marker Assisted Selection (MAS) approaches using only markers associated with known E genes or within regions of the genome thought to influence RM (specific E-gene and expanded E-gene) and two Genomic Prediction (GP) models with distinct marker densities (full GP model and reduced GP model). The GP and expanded E-gene prediction models evaluated in the study produced an average across RM prediction accuracy from 0.93 to 0.94 while the E-gene specific model prediction accuracy was 0.81. The results indicated that the E genes identified in the literature were highly predictive of RM, the greatest prediction accuracies however were obtained through the use of whole genome marker panels. While the results from the initial research were promising, additional research was required to determine if the prediction accuracies could be maintained when predictions were made on segregating lines outside of the years of those contained within the original training dataset. In an attempt to strengthen the prediction accuracies obtained for the early and late maturities, the original training dataset was expanded to include a total of 2,194 segregating lines that were selected from replicated field trials in 2009-2013 having validated RM phenotypes that ranged from RM 0.0 to 8.0. All of the 2,194 segregating lines within the updated training dataset had previously been genotyped using 1,118 genome wide SNP markers. Since it was identified in the preliminary research that prediction accuracies were highest when whole genome marker panels were used, only a full GP model using allele effect estimates for all 1,118 SNP markers was evaluated in this study. The 1,118 SNP marker GP model successfully predicted the RM’s of 1,854 segregating lines in 2014 and 1,465 segregating lines in 2015. The estimated correlation between predicted RM (RMp) and validated RM (RMv) for all segregating lines was 0.95 with an average difference between RMp and RMv of 4 days. Prediction accuracies were again the lowest for segregating lines with RMv earlier than 1.0 and later than 5.0 which we feel was still likely the result of a small number of segregating lines in the training set for those RM groups. Alternative metrics including the frequency of RMp within 0.5 of RMv, f(|RMp-RMv|≤ 0.5) and the frequency of RMp within 0.25 of RMv, f(|RMp-RMv|≤0.25) were developed that indicated that across years, 66% of the segregating lines had RMp that were within 5 days of their RMv and 39% of the segregating lines had RMp that were within 2.5 days or their RMv. The f(|RMp-RMv|≤ 0.5) and f(|RMp-RMv|≤0.25) improved to 73% and 46% respectively when only segregating lines with RMv that ranged from 1.0 – 5.9 were evaluated. While the results from this second round of research proved that genetic information could be used to predict the RM of segregating lines with relatively high accuracy across the maturity groups grown within NA, additional analysis was required to determine if wide scale implementation could be justified within a breeding program. In effort to determine if the prediction accuracies and genotyping costs associated with predicting the RM of segregating lines using genetic information could be justified for wide scale implementation within a breeding program, we evaluated the program wide implementation of RM prediction using basic principles of Operations Research (OR). A simple Microsoft Excel based tool termed the Genomic Prediction Evaluation Tool (GPE tool) was built that allowed all possible cultivar development scenarios that exist within the Iowa State University soybean breeding program to be evaluated to determine both Total Program Cost (TPC) and Relative Breeding Design Efficiency (RBDE). Optimal breeding designs were those designs that both maximized RBDE while minimizing TPC. Two analysis were conducted using the GPE tool. The first analysis (analysis 1) determined the total number of years to reach the final year of replicated field trials as the number of years from the initiation of crossing to the final year of field trials. The second analysis (analysis 2) added a year to the total number of years from crossing to the final year of field trials for those designs that utilized a North American summer crossing block, thus decreasing associated RBDE. Of the optimal breeding designs identified from both analysis, no design was identified that recommended the use of RM prediction to support the cultivar development process, the associated cost of implementation was simply too high. Slight modifications to the current version of the GPE tool should allow the ISU breeding program to identify more efficient breeding designs as compared to the current design that has been implemented to date. The GPE in its current version sets the foundation to build a tool that will provide soybean breeders the ability to appropriately evaluate the potential wide scale implementation of GS to predict complex phenotypes in support of soybean variety development.</p

Busting The Bocage : American Combined Arms Operations In France, 6 June - 31 July 1944

21 cm; 75 ha

Change in elementary school teachers' practice in science in the United States

SIGLEAvailable from British Library Document Supply Centre- DSC:DX170988 / BLDSC - British Library Document Supply CentreGBUnited Kingdo