Simulation Experiments on Efficiencies of Gene Introgression by Backcrossing

Designing a highly efficient backcross (BC) marker-assisted selection (MAS) experiment is not a straightforward exercise, efficiency being defined here as the ratio between the resources that need to be invested at each generation and the number of generations required to achieve the selection. This paper presents results of simulations conducted for different strategies, using the maize genome as a model, to compare allelic introgression with DNA markers through BCs. Simulation results indicate that the selection response in the BC1 could be increased significantly when the selectable population size (N sl) is 100. Selectable population size is defined as the number of individuals with favorable alleles at the target loci from which selection with markers can be carried out on the rest of the genome at nontarget loci, simulations considered the allelic introgression at one to five target loci, with different population sizes, changes in the recombination frequency between target loci and flanking markers, and different numbers of genotypes selected at each generation. For an introgression at one target locus in a partial line conversion, and using MAS at nontarget loci only at one generation, a selection at BC3 would be more efficient than a selection at BC1 or BC2, due to the increase over generations of the ratio of the standard deviation to the mean of the donor genome contribution. With selection only for the presence of a donor allele at one locus in BC1 and BC2, and MAS at BC3, lines with <5% of the donor genome can be obtained with a N sl of 10 in BC1 and BC2, and 100 in BC3 These results are critical in the application of molecular markers to introgress elite alleles as part of plant improvement program

An AKAP-Lbc-RhoA interaction inhibitor promotes the translocation of aquaporin-2 to the plasma membrane of renal collecting duct principal cells

Stimulation of renal collecting duct principal cells with antidiuretic hormone (arginine-vasopressin, AVP) results in inhibition of the small GTPase RhoA and the enrichment of the water channel aquaporin-2 (AQP2) in the plasma membrane. The membrane insertion facilitates water reabsorption from primary urine and fine-tuning of body water homeostasis. Rho guanine nucleotide exchange factors (GEFs) interact with RhoA, catalyze the exchange of GDP for GTP and thereby activate the GTPase. However, GEFs involved in the control of AQP2 in renal principal cells are unknown. The A-kinase anchoring protein, AKAP-Lbc, possesses GEF activity, specifically activates RhoA, and is expressed in primary renal inner medullary collecting duct principal (IMCD) cells. Through screening of 18,431 small molecules and synthesis of a focused library around one of the hits, we identified an inhibitor of the interaction of AKAP-Lbc and RhoA. This molecule, Scaff10-8, bound to RhoA, inhibited the AKAP-Lbc-mediated RhoA activation but did not interfere with RhoA activation through other GEFs or activities of other members of the Rho family of small GTPases, Rac1 and Cdc42. Scaff10-8 promoted the redistribution of AQP2 from intracellular vesicles to the periphery of IMCD cells. Thus, our data demonstrate an involvement of AKAP-Lbc-mediated RhoA activation in the control of AQP2 trafficking

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD

Avoiding lodging in irrigated spring wheat. I. Stem and root structural requirements

A model of the lodging process has been successfully adapted for use on spring wheat grown in North-West Mexico (NWM). The lodging model was used to estimate the lodging-associated traits required to enable spring wheat grown in NWM with a typical yield of 6 t ha−1 and plant height of 0.7 m to achieve a lodging return period of 25 years. Target traits included a root plate spread of 51 mm and stem strength of the bottom internode of 268 N mm. These target traits increased to 54.5 mm and 325 N mm, respectively, for a crop yielding 10 t ha−1. Analysis of multiple genotypes across three growing seasons enabled relationships between both stem strength and root plate spread with structural dry matter to be quantified. A NWM lodging resistant ideotype yielding 6 t ha−1 would require 3.93 t ha−1 of structural stem biomass and 1.10 t ha−1 of root biomass in the top 10 cm of soil, which would result in a harvest index (HI) of 0.46 after accounting for chaff and leaf biomass. A crop yielding 10 t ha−1 would achieve a HI of 0.54 for 0.7 m tall plants or 0.41 for more typical 1.0 m tall plants. This study indicates that for plant breeders to achieve both high yields and lodging-proofness they must either breed for greater total biomass or develop high yielding germplasm from shorter crops

Guidelines and considerations for designing field experiments simulating precipitation extremes in forest ecosystems

1. Precipitation regimes are changing in response to climate change, yet understanding of how forest ecosystems respond to extreme droughts and pluvials remains incomplete. As future precipitation extremes will likely fall outside the range of historical variability, precipitation manipulation experiments (PMEs) are critical to advancing knowledge about potential ecosystem responses. However, few PMEs have been conducted in forests compared to short‐statured ecosystems, and forest PMEs have unique design requirements and constraints. Moreover, past forest PMEs have lacked coordination, limiting cross‐site comparisons. Here, we review and synthesize approaches, challenges, and opportunities for conducting PMEs in forests, with the goal of guiding design decisions, while maximizing the potential for coordination. 2. We reviewed 63 forest PMEs at 70 sites world‐wide. Workshops, meetings, and communications with experimentalists were used to generate and build consensus around approaches for addressing the key challenges and enhancing coordination. 3. Past forest PMEs employed a variety of study designs related to treatment level, replication, plot and infrastructure characteristics, and measurement approaches. Important considerations for establishing new forest PMEs include: selecting appropriate treatment levels to reach ecological thresholds; balancing cost, logistical complexity, and effectiveness in infrastructure design; and preventing unintended water subsidies. Response variables in forest PMEs were organized into three broad tiers reflecting increasing complexity and resource intensiveness, with the first tier representing a recommended core set of common measurements. 4. Differences in site conditions combined with unique research questions of experimentalists necessitate careful adaptation of guidelines for forest PMEs to balance local objectives with coordination among experiments. We advocate adoption of a common framework for coordinating forest PME design to enhance cross‐site comparability and advance fundamental knowledge about the response and sensitivity of diverse forest ecosystems to precipitation extremes.New Hampshire Agricultural Experiment Station, Grant/Award Number: NH00071-M; Northern States Research Cooperative, Grant/Award Number: 14-DG-11242307- 142; National Science Foundation Long-Term Ecological Research, Grant/Award Number: 1637685; USDA Forest Service; University of New Hampshire; NASA, Grant/Award Number: NNX14AD31G; USDA National Institute of Food and Agriculture McIntire- Stennis Project, Grant/Award Number: NH00071-M; U.S. Department of Energy; Office of Science’s Terrestrial Ecosystem Science program; Pacific Northwest National Labs’ LDRD program; MSCA-IF 2015; EU-Horizon2020 program; NSF’s Research Coordination Network Progra

Identification of the remains of King Richard III

In 2012, a skeleton was excavated at the presumed site of the Grey Friars friary in Leicester, the last-known resting place of King Richard III. Archaeological, osteological and radiocarbon dating data were consistent with th

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function