High Yielding Soybean: Genetic Gain and Nitrogen Limitation

The United States and Argentina account for more than 50% of the global soybean production. Closing yield gaps (actual on-farm yield vs. genetic yield potential) would require an improvement in the use of the available resources. Overall, 50-60% of soybean nitrogen (N) demand is usually met by the biological nitrogen fixation (BNF) process. A scientific knowledge gap still exists related to the ability of the BNF process to satisfy soybean N demand at varying yield levels. The overall objective of this project is to study the contribution of N via utilization of varying N strategies under historical and modern soybean genotypes. Two field experiments were conducted during the 2016-2017 growing seasons: Rossville, KS (US) and Oliveros, Santa Fe (ARG). This report focuses on the 2016 results. Twenty-one historical and modern soybean genotypes were utilized with release decades between 1980s and 2010s. All were inoculated and tested under three N management strategies: S1, non-N applied; S2, all N provided by fertilizer; and S3, late-N applied. The genetic improvement of soybean yield from the 1980s to 2010s was an overall increase of 30%, averaging results from US and ARG. Seed N content (N exported in seed) followed a similar trend for yield, while N concen­tration in seed was decreased as yields increased. Regarding N management for geno­types from all release decades, S2 (all N provided by fertilizer) generated up to a 20% increase in yields in the US and 5% in ARG. These results suggest that high yielding soybeans could be limited by N under specific growing conditions to express the yield potential

Soybean: Evaluation of Inoculation

Most of the nitrogen (N) required by a soybean plant is supplied via biological nitrogen fixation (BNF). When BNF is adequately established in the soil, soybean can obtain up to 50 to 75% of its N from the air. This project aims to quantify the response to inoculation for soybean in its second year in a field without previous history of this crop. Due to this objective, a field study was conducted during the 2015 and 2016 growing seasons at Ottawa, KS (East Central experiment field location). The treatments consisted of five different N-management approaches: non-inoculated (NI), inoculated ×1 (I×1), inoculated ×2 (I×2), inoculated ×3 (I×3), and non-inoculated but fertilized with 300 lb N/a (NF) as the main N source. In 2015, yields among treatments did not differ significantly from one another. In 2016, yields ranged from 36 to 59 bushels per acre. Greater yields were recorded when fertilized with 300 lb N/a, while lowest yield was related to the non-inoculated scenario. Treatments presented significant yield difference; however, the scenario with 300 lb N/a did not differ from the inoculated ×3; while the inoculated treatments were not different for the yield factor. In summary, further research should be pursued to be more conclusive as to the best management approach for N in soybeans in an area without history of this crop

Soybean Evaluation of Inoculation: A Three-Year Summary

The relationships between soybean (Glycine max) seed yield and response to nitrogen (N) fertilization have received considerable coverage in scientific literature. This project aims to quantify the response to inoculation for soybean in a field without previous history of this crop (20 years). To address this objective, field studies were conducted during the 2015, 2016, and 2017 growing seasons at the East Central Experiment Field, Ottawa, KS. The treatments consisted of five different N-management approaches: non-inoculated (NI), inoculation at the recommended commercial rate (I1), a double rate of inoculation (I2), a triple rate of inoculation (I3), and non-inoculated but fertilized with 300 lb of N/a (NF). In the 2015 growing season, yields did not statistically differ from one another. In the 2016 growing season, treatment differences were observed and seed yield ranged from 36 to 59 bu/a. In the 2017 growing season, treat­ments showed significant yield difference, with yields ranging from 23 to 52 bu/a, from the NI to the NF treatment, respectively. Further research should be carried out to understand the impact of the inoculation practice and better understand the best management for N in soybean in newly-planted areas

Planting Date by Maturity Group in Kansas: 2016 Season and Three-Year Summary

Optimal planting should be timed to capture a favorable environment (e.g., fall rains and cooler temperatures during grain filling). Five field studies were conducted during the 2014 growing season (Manhattan, Topeka, Ottawa, Parsons, and Hutchinson); five in 2015 (Manhattan, Rossville, Ottawa, Parsons, and Hutchinson); and three in 2016 (Manhattan, Topeka, and Ottawa). This study explores the impact of planting date (early-, mid-, and late-planted) on yield for soybean cultivars from a range of maturity groups (early, medium, and late groups). For 2016, the overall main factor impacting yield across sites was planting date, which increased yields with early-planted soybeans. Based on all 13 sites (2014, 2015, and 2016), maximum soybean yield potential decreased by 0.5 bushels per day of delay on planting date when soybean is planted after April 15. Comparable yield penalties have been documented for other main production regions. In summary, weather patterns dictate soybean yields, especially under dryland conditions. There is no guarantee that any certain planting date will always work out the best when it comes to soybean yields in Kansas

Response of a CMS HGCAL silicon-pad electromagnetic calorimeter prototype to 20-300 GeV positrons

The Compact Muon Solenoid Collaboration is designing a new high-granularity endcap calorimeter, HGCAL, to be installed later this decade. As part of this development work, a prototype system was built, with an electromagnetic section consisting of 14 double-sided structures, providing 28 sampling layers. Each sampling layer has an hexagonal module, where a multipad large-area silicon sensor is glued between an electronics circuit board and a metal baseplate. The sensor pads of approximately 1 cm$^2$ are wire-bonded to the circuit board and are readout by custom integrated circuits. The prototype was extensively tested with beams at CERN's Super Proton Synchrotron in 2018. Based on the data collected with beams of positrons, with energies ranging from 20 to 300 GeV, measurements of the energy resolution and linearity, the position and angular resolutions, and the shower shapes are presented and compared to a detailed Geant4 simulation

Performance of the CMS High Granularity Calorimeter prototype to charged pion beams of 20−-300 GeV/c

The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based on silicon sensors interspersed between lead and copper (or copper tungsten) absorbers. The hadronic section uses layers of stainless steel as an absorbing medium and silicon sensors as an active medium in the regions of high radiation exposure, and scintillator tiles directly readout by silicon photomultipliers in the remaining regions. As part of the development of the detector and its readout electronic components, a section of a silicon-based HGCAL prototype detector along with a section of the CALICE AHCAL prototype was exposed to muons, electrons and charged pions in beam test experiments at the H2 beamline at the CERN SPS in October 2018. The AHCAL uses the same technology as foreseen for the HGCAL but with much finer longitudinal segmentation. The performance of the calorimeters in terms of energy response and resolution, longitudinal and transverse shower profiles is studied using negatively charged pions, and is compared to GEANT4 predictions. This is the first report summarizing results of hadronic showers measured by the HGCAL prototype using beam test data.Comment: To be submitted to JINS

X chromosome inactivation does not necessarily determine the severity of the phenotype in Rett syndrome patients

Rett syndrome (RTT) is a severe neurological disorder usually caused by mutations in the MECP2 gene. Since the MECP2 gene is located on the X chromosome, X chromosome inactivation (XCI) could play a role in the wide range of phenotypic variation of RTT patients; however, classical methylation-based protocols to evaluate XCI could not determine whether the preferentially inactivated X chromosome carried the mutant or the wild-type allele. Therefore, we developed an allele-specific methylation-based assay to evaluate methylation at the loci of several recurrent MECP2 mutations. We analyzed the XCI patterns in the blood of 174 RTT patients, but we did not find a clear correlation between XCI and the clinical presentation. We also compared XCI in blood and brain cortex samples of two patients and found differences between XCI patterns in these tissues. However, RTT mainly being a neurological disease complicates the establishment of a correlation between the XCI in blood and the clinical presentation of the patients. Furthermore, we analyzed MECP2 transcript levels and found differences from the expected levels according to XCI. Many factors other than XCI could affect the RTT phenotype, which in combination could influence the clinical presentation of RTT patients to a greater extent than slight variations in the XCI pattern

Soybean: Genetic Gain × Fertilizer Nitrogen Interaction

The United States (US) and Argentina (ARG) account for more than 50% of the global soybean production. Soybean yields are determined by the genotype, environment, and management practices (G × E × M) interaction. Overall, 50-60% of soybean nitrogen (N) demand is usually met by the biological nitrogen fixation (BNF) process. An unanswered scientific question concerns the ability of BNF process to satisfy soybean N demand at varying yield levels. The overall objective of this project was to study the contribution of N via utilization of different N strategies, evaluating soybean genotypes released in different eras. Four field experiments were conducted during the 2016 season: Ottawa (east central Kansas, US), Ashland Bottoms (central Kansas, US), Rossville (central Kansas, US), and Oliveros (Santa Fe province, Argentina). A wide variety of historical and modern soybean genotypes were used (from the 1980s, 1990s, 2000s and 2010s release decades) in the US and ARG, all tested under three N management strategies (S1: non-N applied but inoculated, S2: all N provided by fertilizer, and S3: late-N applied) and all seeds inoculated. At Ottawa, the study was planted in an area without previous soybean history with yields ranging from 21 to 30 bu/a. Modern genotype (2010) increased yields by 15% relative to the other varieties. As related to the N management approach, higher yields occurred when the N nutrition was based on S2 (overall 10% increase). At Ashland Bottoms, yields ranged from 47 to 65 bu/a, and the 1990s variety out-yielded the rest of the varieties by 13%. There was not statistical significance for N management at this location. At Rossville, yields ranged from 37 to 85 bu/a, with higher yields observed for the modern genotype (released after 2010). Regarding N strategies, S2 increased yields by 18% compared to S1. At ARG, yield ranged from 40 to 74 bu/a, with modern soybean varieties (released after 2010) yielding 34% greater than the rest of the varieties. Nitrogen application S2 increased yields by 5% when compared to the S1 strategy. Relative to yield potential, yield levels in Argentina were similar to those in central Kansas (Ashland Bottoms and Rossville)

High-Yielding Soybean: Genetic Gain × Fertilizer Nitrogen Interaction

The U.S. accounts for 35% of the global soybean production. Potential soybean yields are determined by the interaction of genotype, environment, and management practices (G × E × M). The question “Do high yielding soybean need to be fertilized with nitrogen (N)?” is still a valid one. The overall objective of this project is to study the contribution of N via utilization of varying N strategies under historical and current soybean genotypes. Two field experiments were conducted during the 2015 growing season at Ottawa (east central KS) and at Ashland Bottoms (central KS). Three soybeans varieties were used (1990s = non-RR, 2000s = RR-1, and 2010s = RR-2) under three N systems (non-N applied; late-N, 50 lb N/a; and 550 lb N/a, split in 3 timings) with all seeds inoculated. At Ottawa, the study was planted in an area without soybean history, with yields ranging from 14 to 37 bushels per acre. Superior yields were recorded for the modern soybean variety Roundup Ready (RR-2) relative to the RR-1 and non-RR materials. As related to the N management approach, slightly higher soybean yields occurred when N nutrition was based on fertilizer N application. At the Ashland Bottoms site, yields ranged from 44 to 76 bushels per acre. High yields were with the oldest soybean genotype (non-RR) when N nutrition was based on the fertilizer N application; while low yields were when the N nutrition of the modern soybean variety (RR-2) was based on the inoculation. There was no variety by N factor interaction with yield. The variety (P \u3c 0.05) was the main significant single effect, which presented the following order from high to low productivity: non-RR \u3e\u3e RR-1 = RR-2. A conclusion from the first year of this experiment was the field where soybean had not been previously planted (Ottawa) had a lower yield capacity compared to the site with a soybean history (Ashland Bottoms)

Targeted next generation sequencing in patients with inborn errors of metabolism

Background: Next-generation sequencing (NGS) technology has allowed the promotion of genetic diagnosis and are becoming increasingly inexpensive and faster. To evaluate the utility of NGS in the clinical field, a targeted genetic panel approach was designed for the diagnosis of a set of inborn errors of metabolism (IEM). The final aim of the study was to compare the findings for the diagnostic yield of NGS in patients who presented with consistent clinical and biochemical suspicion of IEM with those obtained for patients who did not have specific biomarkers. Methods: The subjects studied (n = 146) were classified into two categories: Group 1 (n = 81), which consisted of patients with clinical and biochemical suspicion of IEM, and Group 2 (n = 65), which consisted of IEM cases with clinical suspicion and unspecific biomarkers. A total of 171 genes were analyzed using a custom targeted panel of genes followed by Sanger validation. Results: Genetic diagnosis was achieved in 50% of patients (73/146). In addition, the diagnostic yield obtained for Group 1 was 78% (63/81), and this rate decreased to 15.4% (10/65) in Group 2 (χ2 = 76.171; p < 0.0001). Conclusions: A rapid and effective genetic diagnosis was achieved in our cohort, particularly the group that had both clinical and biochemical indications for the diagnosis. © 2016 Yubero et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited